System and method for providing prediction-model-based generation of a graph data model

ABSTRACT

In some embodiments, templates related to each graph data model of a graph data model set for converting non-graph data representations in a non-graph database to graph data representations compatible with a graph database may be obtained. One or more templates and the non-graph data representations may be provided to a neural network for the neural network to predict additional templates. The additional templates may be provided to the neural network as reference feedback for the neural network&#39;s prediction of the additional templates to train the neural network. A collection of non-graph data representations from a given non-graph database may be provided to the neural network for the neural network to generate one or more templates for a given graph data model for converting non-graph data representations in the given non-graph database into graph data representations compatible with a given graph database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to the following applications, filed on evendate herewith: (1) U.S. patent application Ser. No. 16/007,639, entitled“System and Method for Reducing Data Retrieval Delays viaPrediction-Based Generation of Data Subgraphs;” and (2) U.S. patentapplication Ser. No. 16/007,850, entitled “System and Method forReducing Query-Related Resource Usage in a Data Retrieval Process,” eachof which is hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The invention relates to facilitating multi-source-type interoperabilityand information retrieval optimization.

BACKGROUND OF THE INVENTION

As an organization grows, the number of data silos existing within theorganization generally increases, creating many repositories of fixeddata that is “unconnected” to the rest of the organization. Such datasilos often exist because the data format or data source technologiesfail to match a previous or current standard. Large enterprises, forexample, often have inherited and legacy data systems that areincompatible with one another or incompatible with new data systems.Although data migration systems can be used to transfer data fromdifferent data storage types, formats, or IT systems into one datasystem matching a current standard, such large scale data migrationprocesses typically require substantial overhead (e.g., computationalresources, time, etc.) and cause significant disruptions to otherorganizational activities. These and other drawbacks exist.

SUMMARY OF THE INVENTION

Aspects of the invention relate to methods, apparatuses, or systems forfacilitating multi-source-type interoperability and informationretrieval optimization, such as the use of data conversion models tofacilitate a multi-source-type query to multiple data sources ofdifferent data source types, the use of prediction models to generatedata conversion models, the use of prediction models to storetransformed data in temporary data storage in anticipation of requestsfor such data, the optimization of query sets derived from a datarequest to reduce query-related resource usage in a data retrievalprocess, etc.

In some embodiments, machine-learning-model-based generation of a graphdata model may be provided to facilitate conversion of non-graph datarepresentations to compatible graph data representations. As an example,first modeling information related to a first graph data model may beobtained, where the first modeling information includes first templatesfor converting first data representations not compatible with a firstgraph database to graph data representations compatible with the firstgraph database. One or more templates of the first templates and thefirst data representations may be provided to a machine learning model.Based on the foregoing input, the machine learning model may predict oneor more additional templates of the first templates without reliance onthe additional templates. The additional templates (of the firsttemplates) may be provided to the machine learning model as referencefeedback, and the machine learning model may assess its predictionagainst the reference feedback (i.e., the additional templates) andupdate one or more portions of the machine learning model based on theassessment of the prediction. A collection of data representations froma given database may be provided to the machine learning model for themachine learning model to generate one or more templates for a givengraph data model for converting the given database's datarepresentations into graph data representations for a given graphdatabase.

Various other aspects, features, and advantages of the invention will beapparent through the detailed description of the invention and thedrawings attached hereto. It is also to be understood that both theforegoing general description and the following detailed description areexemplary and not restrictive of the scope of the invention. As used inthe specification and in the claims, the singular forms of “a,” “an,”and “the” include plural referents unless the context clearly dictatesotherwise. In addition, as used in the specification and the claims, theterm “or” means “and/or” unless the context clearly dictates otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for facilitating multi-source-typeinteroperability and information retrieval optimization, in accordancewith one or more embodiments.

FIG. 2 illustrates an enterprise environment that includes one or morecomponents of the system, in accordance with one or more embodiments.

FIG. 3 illustrates an example architecture of one or more components ofthe system, in accordance with one or more embodiments

FIG. 4 illustrates an example of a graph in a graph database, inaccordance with one or more embodiments.

FIG. 5 illustrates data representations not compatible with a graphdatabase, a template for converting the non-compatible datarepresentations to graph data representations compatible with the graphdatabase, and graph data representations derived from the non-compatibledata representations, in accordance with one or more embodiments.

FIG. 6 illustrates examples of two different sets of graph datarepresentations generated based on templates from a trained predictionmodel, in accordance with one or more embodiments.

FIG. 7 illustrates an example of a graph data model, in accordance withone or more embodiments.

FIG. 8 illustrates an example of prediction model reasoning, inaccordance with one or more embodiments.

FIG. 9 illustrates an example of similarity predictions by a predictionmodel, in accordance with one or more embodiments.

FIG. 10 illustrates an example of code determining similarity betweennodes of a graph, in accordance with one or more embodiments.

FIG. 11 illustrates examples of the generation of new nodes or edges andthe learning of rules and ontology matches, in accordance with one ormore embodiments

FIG. 12 illustrates a method for providing a prediction-model-basedgeneration of a graph data model, in accordance with one or moreembodiments.

FIG. 13 illustrates a method for reducing data retrieval delays viaprediction-based generation of data subgraphs, in accordance with one ormore embodiments.

FIG. 14 illustrates a flowchart of a method for reducing query-relatedresource usage in a data retrieval process, in accordance with one ormore embodiments.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention. It will beappreciated, however, by those having skill in the art that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other instances,well-known structures and devices are shown in block diagram form inorder to avoid unnecessarily obscuring the embodiments of the invention.

FIG. 1 shows a system 100 for facilitating multi-source-typeinteroperability and information retrieval optimization, in accordancewith one or more embodiments. As shown in FIG. 1, system 100 may includeserver(s) 102, client devices 104 a-104 n, data source(s) 132, or othercomponents. Server 102 may include data management subsystem 112, modelmanagement subsystem 114, request subsystem 116, optimization subsystem118, presentation subsystem 120, electronic storage 122, or othercomponents. Each client device 104 may include any type of mobileterminal, fixed terminal, or other device. By way of example, clientdevice 104 may include a desktop computer, a notebook computer, a tabletcomputer, a smartphone, a wearable device, or other client device. Usersmay, for instance, utilize one or more client devices 104 to interactwith one another, one or more servers 102, or other components of system100. Data sources 132 may include graph data sources 134, unstructured(e.g., typewritten documents), semi-structured (e.g., XML documents,emails, etc.), or structured (e.g., relational database managementsystem (RDBMS) information, lightweight directory access protocol (LDAP)information, etc.) data sources 136, and other data sources 138. Thedata sources may include various databases or other sources of data. Insome embodiments, a single database may include one or more data sources132. It should be noted that, while one or more operations are describedherein as being performed by particular components of server 102, thoseoperations may, in some embodiments, be performed by other components ofserver 102 or other components of system 100. As an example, while oneor more operations are described herein as being performed by componentsof server 102, those operations may, in some embodiments, be performedby components of client device 104.

In some embodiments, with respect to FIG. 2, enterprise environment 200may be an environment for one or more components of system 100.Enterprise environment 200 may include an applications and analyticsportion 202, an enterprise “data lake” portion 204, a virtual portion206, or other portions. Server 102 may be configured to receive varioustypes of information such as reports 208, analytics 210, machine-learnedor data-mined information 212, unstructured information 214 (e.g.,typewritten documents), semi-structured information 216 (e.g., XMLdocuments, emails, etc.), structured information 218 (e.g., RDBMSinformation, LDAP information, etc.), or other information. Server 102may receive such information from one or more client devices 104, forexample, running various applications or analytics operations, or fromother sources.

In some embodiments, with respect to FIG. 3, architecture 300 may be anarchitecture for one or more components of system 100. As shown in FIG.3, server 102 may include a metadata extractor 302, a text extractor304, a graph extractor 306 (e.g., a resource description framework (RDF)extractor), a machine learning component 308, a geospatial index 310, agraph index 312, a text index 314, a relational mapping component 316, aquery engine 318, or other components. In some embodiments, one or moreof these components may be, be included in, or perform operationsassociated with, the one or more of the server components shown in FIG.1 and described herein. For example, metadata extractor 302, textextractor 304, graph extractor 306, or other components may be, or beincluded in data management subsystem 112 shown in FIG. 1. Machinelearning component 308 may be, or be included in model managementsubsystem 114 shown in FIG. 1. Geospatial index 310, graph index 312,text index 314, relational mapping component 316, query engine 318, orother components may be, or be included in model management subsystem114, request subsystem 116, or optimization subsystem 118 shown in FIG.1, or other subsystems. In some embodiments, server 102 is associatedwith a document storage system 320.

In some embodiments, with respect to FIG. 3, server 102 may receivegraph data, unstructured data, information from a relational databasemanagement system, or data from other sources 134. As described herein,the components of server 102 (e.g., query engine 318) may perform one ormore queries to obtain relevant results. Based on templates generated bythe system, or other information (e.g., information from geospatialindex 310, graph index 312, text index 314, relational mapping component316), server 102 may convert data representations obtained from thequeries into a graph form (or other form) via one or more dataconversion models, as described herein elsewhere.

In some embodiments, system 100 may facilitate multi-source-typeinteroperability among different data source technologies or standardsvia the generation of a data conversion model or other data model, whichare configured to convert data representations of one data source intodata representations compatible with another data source (or viceversa). In some embodiments, system 100 may utilize such data conversionmodels to facilitate a multi-source-type query to multiple data sourcesof different data source types by using the data conversion models toconvert non-compatible query results (e.g., of different data sourcetypes) into a set of results compatible with a target data source. Inthis way, for example, system 100 may obviate the need for a company orother entity to overhaul its legacy or current databases in favor of newor different data source technologies or standards. In one use case,system 100 may provide on-the-fly conversions of data representationsfrom one or more data sources of different data source types via one ormore such data conversion models.

In some embodiments, system 100 may obtain one or more templates forconverting data representations of a first data source type (e.g., arelational model type or other data source type) into datarepresentations of a second data source type (e.g., a graphical modeltype or other data source type), and create or modify a data conversionmodel based on the obtained templates. As an example, the templates mayinclude instructions for converting of data characteristicscorresponding to the first data source type (e.g., row or columnattributes and values specific to a particular SQL data source or otherdata source) to data characteristics corresponding to the second datasource (e.g., graph attributes and values specific to a graph datasource or other data source). As a further example, system 100 mayprocess the templates to determine patterns (e.g., regular expressionsor other patterns) or rules (associated with the templates) for matchinga data representation of the first data source type to at least one ofthe templates that can be used to convert the non-graph datarepresentation to a data representation of the second data source type.System 100 may then generate the data conversion model to incorporatethe patterns, rules, or other modeling information as part of the dataconversion model (e.g., such that the data conversion model includes orindicates such templates, its patterns or rules, etc.). In someembodiments, system 100 may utilize one or more prediction models (e.g.,neural networks or other machine learning models) to generate one ormore graph data models configured to convert data representations (notcompatible with a particular graph database) into graph datarepresentations compatible with the graph database or to generate one ormore other data conversion models, as described herein elsewhere.

In some embodiments, system 100 may facilitate reduction of delay forproviding a sufficient response to a request or improve efficiency oftemporary data storage or other computer resource usage. System 100 mayfacilitate reduction of delay or improve efficiency, for example, viaprediction of requests and temporary storage of query results related tothe predicted requests in graph form, via selective obtainment ortemporary storage of subsets of the query results related to thepredicted requests, via query set optimization, or other techniques. Asan example, a request for query results may be predicted, a subset ofresults may be obtained responsive to the request prediction, and thesubset of results may be stored at a server cache, a web cache, memorycache, or other temporary data storage (e.g., electronic storage 122).The subset of results may be converted into one or more sub-graphs(e.g., if the results are not in a suitable graph form), and thesub-graphs may be stored in the temporary data storage. When thepredicted request (or a future request matching the predicted request)does occur, one or more of the subgraphs may be obtained from thetemporary data storage (e.g., in lieu of having to obtain, and possiblyconvert from non-graph form to graph form, the subset of results throughother data storage with significantly greater delay) and used to respondto the occurred predicted request. In this way, for example, thetemporary storage of results in their converted form (prior toparticular requests occurring) may significantly decrease latency orother delays for sufficiently responding to requests.

In some embodiments, system 100 may reduce query-related resource usagein a data retrieval process by optimizing a query set derived from adata request, such as a query set into which a graph query (or otherquery related to the data request) is transformed. In some embodiments,such query set optimizations may include removal of a query operatorlinking multiple queries from a query set, merging of multiple queriesof a query set into a single query, removal of one or more queries froma query set, or other optimizations. Such optimizations may be performedbased on a prediction of one or more satisfiability issues (e.g.,related to combining results derived from certain queries),incompatibility issues, or other issues to avoid or mitigate such issuesor negative impacts of such issues. In some embodiments, in response toobtaining a graph query related to a data request, system 100 maytransform the graph query to a query set having multiple queries andquery operators linking the queries (e.g., unions, joins, or other queryoperators). Based on a prediction of a satisfiability issue (related tocombining results derived from two of the queries), system 100 mayremove a query operator that links the two queries from the query set orperform other optimizations on the query set to update the query set. Assuch, when system 100 executes the updated query set to satisfy thegraph query (and, thus, the data request), system 100 may avoid ormitigate the satisfiability issue or the negative impacts of thesatisfiability issue, such as (i) the waste of resources used to executeone or more portions of the query set and attempting to combineincompatible results derived from such query set execution, (ii) thedelay resulting from such execution and attempts, or (iii) othernegative impacts.

In some embodiments, system 100 may facilitate prediction-model-based(i) generation of data models (e.g., data conversion models, graph datamodels, etc.), (ii) obtainment or storage of results related to futurerequests or other information, (iii) generation or performance ofqueries, (iv) query set optimization, or (v) other operations. Theprediction models may include neural networks, other machine learningmodels, or other prediction models. As an example, neural networks maybe based on a large collection of neural units (or artificial neurons).Neural networks may loosely mimic the manner in which a biological brainworks (e.g., via large clusters of biological neurons connected byaxons). Each neural unit of a neural network may be connected with manyother neural units of the neural network. Such connections can beenforcing or inhibitory in their effect on the activation state ofconnected neural units. In some embodiments, each individual neural unitmay have a summation function that combines the values of all its inputstogether. In some embodiments, each connection (or the neural unititself) may have a threshold function such that the signal must surpassthe threshold before it is allowed to propagate to other neural units.These neural network systems may be self-learning and trained, ratherthan explicitly programmed, and can perform significantly better incertain areas of problem solving, as compared to traditional computerprograms. In some embodiments, neural networks may include multiplelayers (e.g., where a signal path traverses from front layers to backlayers). In some embodiments, back propagation techniques may beutilized by the neural networks, where forward stimulation is used toreset weights on the “front” neural units. In some embodiments,stimulation and inhibition for neural networks may be more free-flowing,with connections interacting in a more chaotic and complex fashion.

In some embodiments, system 100 may obtain (i) modeling informationrelated to data models (e.g., templates, its patterns or rules, or otherinformation related to data conversion models, graph data models, etc.),(ii) graph information related to nodes or edges of a graph or otherinformation related to data points or relationships of other datasources, (iii) query information (e.g., graph queries, SQL or othernon-graph queries, query sets derived from the graph queries or thenon-graph queries, etc.), (iv) optimization or issue information (e.g.,information indicating query set optimization logic or otheroptimizations, information indicating potential issues related tocombining results of particular queries, etc.), or other traininginformation (e.g., unstructured documents, semi-structured documents,structured documents, or other information). System 100 may cause one ormore prediction models to be trained based on the training informationto generate (i) one or more data models, graphs or other datastructures, query sets, or other information or (ii) one or morepredictions related thereto such as predicted patterns, rules,templates, optimization logic, satisfiability issues, graph node or edgeattributes/values or other attributes/values, etc. As an example, thenumber of information items (e.g., patterns, rules, templates, graphs,query sets, or other training information items) used to train theprediction models may be 500 or more information items, 1000 or moreinformation items, 10000 or more information items, 100000 or moreinformation items, 1000000 or more information items, or other number ofinformation items.

Data Model and Data Generation

In some embodiments, model management subsystem 114 may be configured to(i) generate one or more data models (e.g., data conversion models,graph data models, etc.) or information related thereto, (ii) predictpatterns, rules, templates, or other modeling information for such datamodels, or (iii) perform other options. In some embodiments, modelmanagement subsystem 114 may obtain one or more templates for convertingdata representations of a first data source type (e.g., a relationalmodel type or other data source type) into data representations of asecond data source type (e.g., a graphical model type or other datasource type), and create or modify a data conversion model based on theobtained templates. As an example, the templates may includeinstructions for converting data characteristics corresponding to thefirst data source type (e.g., row or column attributes and valuesspecific to a particular SQL data source or other data source) to datacharacteristics corresponding to the second data source (e.g., graphattributes and values specific to a graph data source or other datasource). As a further example, model management subsystem may processthe templates to determine patterns (e.g., regular expressions or otherpatterns) or rules (associated with the templates) for matching a datarepresentation of the first data source type to at least one of thetemplates that can be used to convert the data representation of thefirst data source type to a data representation of the second datasource type. Model management subsystem 114 may then generate the dataconversion model to incorporate the patterns, rules, or other modelinginformation as part of the data conversion model.

As an example, with respect to FIG. 4, non-graph data representations(e.g., table fields, rows or columns of a table, etc.) may be convertedinto graph data representations such as those in graph 400 (e.g., nodesor edges of graph 400), which includes graph data representationsrelated to motion pictures. As shown in FIG. 4, graph 400 includesvarious nodes 402 and edges 404 connecting nodes 402. In this example,two different motion picture nodes 402 corresponding to two differentmotion pictures TT1583420 and TT016222 are connected to label nodes 402(e.g., Movie Name 1 and Movie Name 2) for the motion pictures, directornodes NM000158 and NM000709, a production company node C09940938, and arelease date node Dec. 22, 2000. These connections are illustrated withthe various edges 404 between individual nodes 402. Director nodesNM000158 and NM000709, and production company node C09940938, are alsoconnected to corresponding label nodes 402 (e.g., Person 1, Person 2,and Company Name) by edges 404.

As a further example, with respect to FIG. 5, non-graph datarepresentations in table 500 (e.g., from a SQL or other relationaldatabase) may be converted into graph data representations of graph 504using template 502 (or templates 502). As indicated in FIG. 5, template502 may include instructions for converting rows or columns (e.g., oftable 500) to graph nodes or edges (e.g., of resulting graph 504) (orvice versa, in some embodiments). In this example, table 500 includesinformation for two movies titled Movie 3 and Movie 4, such asidentification codes 506, titles 507, release years 508, box officecountry names 510, country codes 512, and gross earnings 514. Template502 (which is shown in FIG. 5 in a graphical form) includes“placeholder” nodes 516 for a movie title, an identification code, arelease year, a box office, a box office country, a box office countrylabel, a box office country code, and gross earnings in that country.Nodes 516 are linked by corresponding edges 518, which indicate therelationships between such nodes 516. Based on the instructions oftemplate 502 (e.g., nodes 516), non-graph data representations of table500 for Movie 3 are converted into graph data representations (e.g.nodes 520) of graph 504 illustrates nodes 520.

Returning to FIG. 1, data management subsystem 112 may be configured to(i) generate one or more graphs or other data structures (e.g., SQL datastructures, other non-graph data structures, etc.), (ii) predictinformation for nodes, edges, or other portions of such data structures,or (iii) perform other options. In some embodiments, data managementsubsystem 112 may utilize one or more graph data models to create ormodify a graph from non-graph data representations (e.g., stored in SQLtables or other data sources). As an example, data management subsystem112 may use a graph data model to convert non-graph data representationsinto graph representations (e.g., compatible with a particular graphdatabase) to create a new graph or to supplement/modify an existinggraph in the graph database.

In some embodiments, for one or more graphs, request subsystem 116 maygenerate one or more path queries for finding one or more paths betweennodes of a graph (e.g., to determine all paths between two graph nodes,to determine the shortest path between two graph nodes or apredetermined number of the most shortest paths between the two graphnodes, etc.). In some embodiments, request subsystem 116 may generatethe path queries to include restrictive parameters for the paths thatsuch path queries will return. As an example, one such path query mayrestrict the results to paths associated with transactions greater thana specified monetary amount (e.g., all nodes or edges in the shortestpath must be associated with transactions greater than $10K or otherspecified monetary amount). As another example, a path query mayrestrict the results to paths associated with a lifecycle (e.g., productlife cycle, animal life cycle, document life cycle, etc.). As yetanother example, a path query may restrict the results to paths from amovie node to nodes representing the production company and its parentcompanies (e.g., the movie Hitchcock is produced by Fox SearchlightPictures which is owned by Fox Studios which is owned by 21st CenturyFox). In some embodiments, request subsystem 116 may determine a queryplan to respond to a data request based on such paths returned by suchpath queries. As an example, based on such path information, requestsubsystem 116 may determine one or more queries to handle a data request(e.g., obtained from a user device or predicted as described herein),determine which graphs or graph databases are to be target sources forhandling the data request, determining costs associated with such targetsources, etc. Request subsystem 116 may then create or select a queryplan for the data request based on such determinations (e.g., byincorporating the target sources in the query plan, prioritizing thequeries or target sources based on the cost information, etc.).

In some embodiments, prediction models (e.g., neural networks, othermachine learning models, or other prediction models) may be utilized tofacilitate the generation of graph data models or other data models, thegeneration of graphs or other data structures, the prediction ofinformation for such data models or data structures, the determinationof query plans, or other operations. Training data used to train theprediction models may include (i) inputs to be provided to a predictionmodel (e.g., inputs provided to and processed by other prediction modelsor other inputs), (ii) reference outputs that are to be derived from aprediction model's processing of such inputs (e.g., user-confirmed oruser-provided outputs, outputs confirmed through one or more predictionmodels' processing of such inputs, outputs confirmed multiple times byprocessing of such inputs by respective sets of prediction models, orother reference outputs), (iii) reference indications of outputs thatare not to be derived from a prediction model's processing of suchinputs (e.g., user indications that such outputs are inaccurate or otherreference indications), or (iv) other training data.

In some embodiments, model management subsystem 114 may obtain modelinginformation related to a graph data model set (that includes one or moregraph data models), a collection of data representations for each of thegraph data models to convert into graph data representations (e.g.,compatible with a particular graph database), or other information.Model management subsystem 114 may provide the modeling information, thecollections of data representations, or other information to aprediction model to train the prediction model.

As an example, the modeling information may include (i) templates forconverting data representations (e.g., non-graph data representations orother data representations not compatible with a given graph database)to graph data representations compatible with the graph database, (2)patterns or rules (associated with the templates) for matching anon-compatible data representation to at least one of the templates thatcan be used to convert the non-compatible data representation to acompatible graph data representation, or (3) other modeling information.For each graph data model of the graph data model set and thenon-compatible data representations (that the graph data model isconfigured to convert), model management subsystem 114 may provide oneor more templates of the graph data model's templates and thenon-compatible data representations to the prediction model for theprediction model to predict one or more additional templates of thegraph data model's templates. In one use case, for example, even wheresuch additional templates already exist as part of the graph data model,the prediction model may be caused to predict the additional templatesas part of the training of the prediction model. Thus, in one use case,the prediction model may predict the additional templates withoutreliance on the additional templates (e.g., without reliance on at leastsome of the additional templates, without reliance on any of theadditional templates, etc.).

As a further example, for each graph data model of the graph data modelset, model management subsystem 114 may provide the additional templatesof the graph data model's templates to the prediction model as referencefeedback for the prediction model's prediction of the additionaltemplates to train the prediction model. The prediction model may usethe additional templates as “reference templates” to assess itsprediction of the additional templates (e.g., templates generated by theprediction model based on other templates provided as input to theprediction model). Based on its assessment of its prediction, theprediction model may update one or more portions of the prediction model(e.g., by adjusting weights of the prediction model's parameters orother portions of the prediction model in accordance with whether itsprediction was accurate or how accurate its prediction was). In one usecase, where the prediction model is a neural network, the neural networkmay update one or more layers of the neural network based on the neuralnetwork's assessment of its prediction of the additional templates. Asan example, the neural network may use forward or back propagationtechniques to reset or modify weights of neural units in one or morelayers (e.g., hidden layers between the input and output layers of theneural network or other layers of the neural network) based on theaccuracy of its prediction (e.g., whether or how accurate its predictionof one or more of the additional templates was).

In some embodiments, upon a prediction model being trained (or updatedbased on such training), model management subsystem 114 may use theprediction model to generate a graph data model. As an example, modelmanagement subsystem 114 may cause the prediction model to generate thegraph data model or information usable to generate the graph data model(e.g., templates, patterns, rules, or other information for generatingthe graph data model). As a further example, with respect to convertingnon-graph data representations into graph data representations, modelmanagement subsystem 114 may provide a collection of non-graph datarepresentations from a non-graph database as input to the predictionmodel. Responsive to such input, the prediction model may output one ormore templates, patterns, rules, or other information for the graph datamodel. Model management subsystem 114 may use the templates, itspatterns or rules, or other information to generate the graph data modelsuch that the graph data model is configured for converting thenon-graph data representations (from the non-graph database) into graphdata representations compatible with a graph database.

In some embodiments, data management subsystem 112 may utilize one ormore prediction models to predict information for nodes, edges, or otherdata representations to generate a graph or other data structure, suchas creating the data structure, modifying one or more portions of thedata structure, or supplementing the data structure based on thepredicted information for the nodes, edges, or other datarepresentations. In some embodiments, data management subsystem 112 mayobtain graph information related to nodes or edges of a graph or otherinformation related to data points or relationships of other datasources, and provide the graph information or other such information toa prediction model to train the prediction model to predict informationfor additional or replacement nodes, edges, or other datarepresentations (e.g., to create a new graph or other data structure, tomodify an existing graph or other data structure, to supplement anexisting graph or other data structure, etc.).

In some embodiments, data management subsystem 112 may obtain one ormore data representation sets and provide the data representation setsto a prediction model to train the prediction model. As an example, adata representation set may include nodes, edges, or other datarepresentations, and, for each data representation set of the datarepresentation sets, data management subsystem 112 may provide one ormore nodes or edges of the data representation set as input to theprediction model for the prediction model to predict one or moreadditional nodes or edges of the data representation set. As usedherein, providing nodes or edges may refer to providing data representedby the nodes or edges. In one use case, for example, even where suchadditional nodes or edges already exist as part of the datarepresentation set (e.g., as part of an existing graph), the predictionmodel may be caused to predict the additional nodes or edges as part ofthe training of the prediction model. Thus, in one use case, theprediction model may predict the additional nodes or edges withoutreliance on the additional nodes or edges (e.g., without reliance on atleast some of the additional nodes or edges, without reliance on any ofthe additional nodes or edges, etc.).

As a further example, for each data representation set of the datarepresentation sets, data management subsystem 112 may provide theadditional nodes or edges of the data representation set to theprediction model as reference feedback for the prediction model'sprediction of the additional nodes or edges to train the predictionmodel. The prediction model may use the additional nodes or edges as“reference nodes or edges” to assess its prediction of the additionalnodes or edges (e.g., nodes or edges generated by the prediction modelbased on other nodes or edges provided as input to the predictionmodel). Based on its assessment of its prediction, the prediction modelmay update one or more portions of the prediction model (e.g., byadjusting weights of the prediction model's parameters or other portionsof the prediction model in accordance with whether its prediction wasaccurate or how accurate its prediction was). In one use case, where theprediction model is a neural network, the neural network may update oneor more layers of the neural network based on the neural network'sassessment of its prediction of the additional nodes or edges.

In some embodiments, upon a prediction model being trained (or updatedbased on such training), data management subsystem 112 may use theprediction model to create or modify data representations for a new orexisting data structure. As an example, data management subsystem 112may use the prediction model to create new nodes or edges for a new orexisting graph, modify one or more nodes or edges of an existing graph,or perform other operations. In some embodiments, data managementsubsystem 112 may perform a traversal of a graph to process nodes oredges of the graph (e.g., by causing the prediction model to traversethe graph and process the nodes and edges as input during the traversal,by causing one or more agents to crawl the graph to extract the graphnodes during the traversal and providing the extracted nodes or edges asinput to the prediction model, etc.). Responsive to obtaining such inputresulting from the traversal, the prediction model may generate newnodes or edges for the graph (e.g., as additional nodes or edges for thegraph, as replacement nodes or edges to replace existing nodes or edgesin the graph, etc.).

In some embodiments, responsive to a prediction model's generation of adata representation (e.g., node, edge, or other data representation),data management subsystem 112 may automatically add the datarepresentation to a graph or other data structure without a user input(i) subsequent to the generation of the data representation and (ii)indicating whether or not the data representation is to be added. As anexample, data management subsystem 112 may add the data representationas a new data representation for the data structure, add the datarepresentation as a replacement data representation for an existing datarepresentation of the data structure, or otherwise modify the datastructure based on the data representation.

On the other hand, in some embodiments, the addition of the new datarepresentation to a graph or other data structure (or a determinationnot to add the new data representation) may be based on such asubsequent user input. As an example, a prediction model may generate anode or edge, and data management subsystem 112 may obtain a node oredge from the prediction model and provide a notification to a userregarding the node or edge. In one use case, the notification mayinvolve a prompt to review the node or edge, a prompt to confirm or denythe use of the node or edge as a new or replacement node or edge for thegraph, or other notification. Responsive to a user confirmation to addthe node or edge (e.g., as a new node or edge, as a replacement node oredge, etc.), data management subsystem 112 may add the new or edge tothe graph. Alternatively, responsive to a user declination with respectto adding the node or edge, data management subsystem 112 may determinenot to add the new node or edge to the graph.

In some embodiments, data management subsystem 112 may provide anindication of the user confirmation or user declination as referencefeedback regarding the prediction model's generation of the node oredge. The prediction model may use the reference feedback to assess itsprediction of the node or edge. Based on its assessment of itsprediction, the prediction model may update one or more portions of theprediction model (e.g., by adjusting weights of the prediction model'sparameters or other portions of the prediction model in accordance withwhether its prediction was accurate or how accurate its prediction was).In one use case, where the prediction model is a neural network, theneural network may update one or more layers of the neural network basedon the neural network's assessment of its prediction of the additionalnodes or edges.

Continuing with the information for Movie 3 and Movie 4 from FIG. 5,FIG. 6 shows examples of two different graph data representations 600and 602 generated based on templates from the trained prediction model,in accordance with one or more embodiments. Graph data representations600 and 602 may be roughly thought of as updates to graph datarepresentation 504 from FIG. 5 generated because of additional trainingof the prediction model. Graph data representations 600 and 602 may begenerated by data management subsystem 112 (FIG. 1) based on templatesgenerated by the prediction model. For example, graph datarepresentation 600 includes nodes 520 and edges 518 related to Movie 3shown in graph data representation 504 from FIG. 5. Graph datarepresentation 600 also includes nodes 604 and edges 606 related to anadditional box office (e.g., the Australian box office) for Movie 3. Theinformation associated with nodes 604 and edges 606 was included in datarepresentation 500 from FIG. 5, but not in graph data representation504. However, training of the prediction model may cause the predictionmodel to generate additional templates, and these additional templatesmay be used to generate additional graph data representations such asgraph data representation 600.

Graph data representation 602 provides a further example. Graph datarepresentation 602 includes nodes 520 and 604, and edges 518 and 606related to Movie 3. Graph data representation 602 also includes nodes608 and edges 610 related to Movie 4. Again here, the informationassociated with nodes 608, and edges 610 was included in datarepresentation 500 from FIG. 5, but not in graph data representations504 or 600. However, training of the prediction model may cause theprediction model to generate additional templates, and these additionaltemplates may be used to generate additional graph data representationssuch as graph data representation 602.

In one use case, with respect to FIG. 7, data model 700 is associatedwith a movie 702. Data model 700 includes a document node 704, a placenode 706, a person node 708, and an organization node 710. Document node704 may include or be associated with (e.g., via other edges and nodesnot shown in data model 700) information, such as movie reviews, a moviescript, a movie summary, or other information. Place node 706 mayinclude or be associated with information such as a filming location, alocation depicted in the movie, or other information. Person node 708may include or be associated with actors in the movie, an author of thescript, a director of the movie, or other information. Organization node710 may include or be associated with a production company responsiblefor the movie, or other information. The graph data model 700 may beused to perform logic reasoning.

In another use case, with respect to FIG. 8, a prediction model may beused to form data relationships via reasoning. At step 802, nodes 806,808, and 810 for two different actors (Actor 1, Actor 2) and a director(Director 1), nodes 812 and 814 for two different movies (Movie 5, Movie6), and corresponding edges 816 may be identified. At step 804, theprediction model may reason 820 that, inversely, if a movie includes anactor, that actor is an actor of the movie. Phrased another way, if anedge 816 connects a movie to an actor, there must be an inverse edge 820that connects the actor to the movie. Similar reasoning 820 applies fora director. In addition, the prediction model may reason 822 (andestablish corresponding edges indicating) that the actor or directorworked on the movie. Since the prediction model knows who worked on themovie, the prediction model may reason 824 (and establish correspondingedges indicating) that two people who worked on the same movie must becoworkers. Finally, step 804 illustrates how, since the prediction modelhas established relationships between coworkers, the prediction modelmay reason 826 (and establish corresponding edges indicating) thatactors and directors are connected to each other through one or morecoworkers.

In another use case, with respect to FIG. 9, data-model-based reasoningand/or supervised learning by the prediction model may allow theprediction model to predict which movies (in the examples used herein)are similar to each other. For example, a first view 900 of FIG. 9illustrates Movie 5 and Movie 7, which are both similar to Movie 6. Insome embodiments, as shown in a second view 902 of FIG. 9, theprediction model may be trained using supervised learning. As describedabove, a given node or edge from the prediction model may be added to agraph based on a user confirmation with respect to adding the given nodeor edge. An indication of the user confirmation may be provided to theprediction model by model management subsystem 114 (FIG. 1) as referencefeedback regarding the prediction model's generation of the given nodeor edge. In some embodiments, a user declination with respect to addingthe given node or edge may be obtained. Responsive to a userdeclination, the given node or edge may not be added to the graph. Anindication of the user declination may be provided to the predictionmodel as reference feedback regarding the prediction model's generationof the given node or edge. As shown in view 902, nodes 904, 906, and 908for Actor 1, Director 2, and a keyword are connected to a node 910 forMovie 5 by corresponding edges 912, 914, and 916. In some embodiments,the prediction model may be configured to propose a rating node 918(connected via edge 920) for Movie 5 and a classification node 922(connected via edge 924) for Movie 5 that may be confirmed or denied bya user.

In some embodiments, a similarity value or other measures of similaritybetween nodes may be determined. As an example, with respect to FIG. 10,programming code may be configured to determine a similarity valuebetween nodes of a graph. In this example, the nodes correspond toMovies 5, 6, and 7 shown in FIG. 9. As shown in FIG. 10, a predictionmay be used to predict that Movie 6 has a similarity value of 0.323291relative to Movie 5, Movie 7 has a similarity value of 0.290015 relativeto Movie 5, and Movie 8 has a similarity value of 0.159687 relative toMovie 5.

In some embodiments, model management subsystem 114 may cause aprediction model to construct new nodes or edges, learn rules, or learnontology matches. Keeping with the movie theme, FIG. 11 shows examples1100, 1102, and 1104 of generating new nodes or edges, learning rules,and learning ontology matches respectively, in accordance with one ormore embodiments. As shown in example 1100, the prediction model maygenerate nodes such as a film and its corresponding title, the year thefilm was produced, the film's director, the film's producer, and one ormore actors who starred in the film. In some embodiments, the predictionmodel may generate these nodes based on text provided to the predictionmodel or other information. (In some embodiments, as shown in FIG. 3,the text may have gone through metadata extraction, text extraction,graph extraction, or other extraction processes performed by modelmanagement subsystem 114 or other subsystems of server 102.) As shown inexample 1102, the prediction model may learn rules. The prediction modelmay learn rules based on the nodes and edges of graphs, pathways throughthe nodes, or other information. In this example, the prediction modelmay learn that if a film is associated with violence, the film may be acrime genre film. Finally, as shown in example 1104, the predictionmodel may learn ontology matches. Ontology matches may include differentwords that refer to the same or similar concepts. As shown in thisexample, the prediction model may learn that that “Actor” and “MovieStar” correspond to the same concept. The ontology matching uses threedifferent kinds of similarity metrics to find mappings between terms:syntactic, semantic and structural. Syntactic similarity evaluates thesimilarity of two terms based on the characters in the labels usingtechniques such as edit distance, fuzzy string matching or trigramcosine similarity. This would detect similarity between the terms“birthday” and “birthdate.” Semantic similarity takes the meaning of thelabels into account either by utilizing a manually curated lexicaldatabase (e.g., WordNet) or a separately trained word embedding model.Word embeddings map words to a vector representation based on the usageof words such that words used in similar ways in the training datasetwill be have similar representations. This would detect the similarityof labels that are syntactically distinct like “Actor” and “Movie Star.”Finally, structural similarity inspects how terms are defined withintheir schemas. For example, one schema might define the relationship“starredIn” between “Actor” and “Film” concepts, whereas the otherschema defines the relationship “workedOn” between “MovieStar” and“Movie.” Once the mappings between concepts are established(“Actor”-“MovieStar” and “Film”-“Move” mappings). then the similarity of“starredIn” and “workedOn” will be detected based on the relationshipshaving the same source and target types.

In some embodiments, request subsystem 116 may utilize one or moreprediction models to determine one or more query plans. In someembodiments, request subsystem 116 may obtain path information (e.g.,paths returned by path queries as described herein) for one or moregraphs or graph databases, query plan information (e.g., indicatingprior query plans, actual costs for executing the prior query plans,etc.), or other information from one or more historical databases orother sources. Request subsystem 116 may provide the path information,the query plan information, or other information to a prediction modelto train the prediction model to predict information for one or morequery plans to be used to respond to one or more requests (e.g.,requests from users, predicted requests or other automatically-generatedrequests, etc.). In some embodiments, upon a prediction model beingtrained (or updated based on such training), request subsystem 116 mayuse the prediction model to generate one or more query plans (e.g., inreal-time in response to requests from users, in response to predictionof requests, etc.).

Query Prediction, Storage, and Response

In some embodiments, request subsystem 116 may be configured to make aprediction that a data request will occur in the future. As an example,the request may include a query submission (or a client-initiatedquery), an update request related to the client-initiated query, orother request. In some embodiments, request subsystem 116 may predict arequest for query results and obtain a subset of results responsive tothe request prediction. Data management subsystem 112 may cause thesubset of results to be stored in a temporary data storage (e.g., at aserver cache, a web cache, memory cache, or other temporary datastorage). In some embodiments, data management subsystem 112 may convertthe subset of results into one or more subgraphs (e.g., if the resultsare not in a suitable graph form) and store the sub-graphs in thetemporary data storage. When the predicted request (or a future requestmatching the predicted request) does occur, request subsystem 116 mayobtain one or more of the subgraphs from the temporary data storage anduse the obtained subgraphs to respond to the occurred predicted request.In this way, for example, the temporary storage of results in theirconverted form (prior to particular requests occurring) maysignificantly decrease latency or other delays for sufficientlyresponding to requests.

In some embodiments, request subsystem 116 may predict that a requestwill occur in the future based on prior queries (e.g., prior queriescompatible with a graph data model or other prior queries). As anexample, the request prediction may be based on request historyinformation, such as information indicating one or more prior queries,information indicating respective frequencies of requests (e.g., afrequency of each of the prior queries, update requests related to theprior queries, etc.), information regarding users or client devices thatinitiated prior requests, or other information. In one scenario, atleast some of the requested query results may be obtained based on therequest prediction prior to the request being obtained from a clientdevice in the future. The obtained query results may be stored (e.g., ina temporary data storage, such as a server cache, a web cache, memorycache, or other temporary data storage) in anticipation of the requestoccurring in the future so that the stored query results can be utilizedto respond to the future request upon its occurrence.

In some embodiments, in response to a prediction of a data request,request subsystem 116 may generate one or more graph queries based onone or more parameters of the predicted data request. As an example, theparameters may include one or more search parameters such as keywords, acontent item or identifier/location thereof (e.g., a content ID, ahyperlink or other pointer to the content item, etc.), logical operators(e.g., logical AND operators, logical OR operators, logical NOToperators, or other logical operators), or other parameters. In one usecase, where the content item is an image, the image may be used for asearch for similar images, content items having the same image orsimilar images, content items having similar concepts as concepts in theimage, or other results. In another use case, where the content item isa video, the video may be used for a search for similar videos, contentitems having the same video or similar videos, content items havingsimilar concepts as concepts in the video, or other results.

In some embodiments, based on a graph data model, request subsystem 116may convert at least one of the graph queries into one or more non-graphqueries (e.g., compatible with one or more SQL or other non-graphdatabases). As an example, the non-graph queries may be performed toobtain a data subset from one or more non-graph databases. In someembodiments, at least one of the graph queries may be performed toobtain a data subset from one or more graph databases. As an example,some of the graph queries may be converted to non-graph queries toobtain results (related to the predicted data request) stored at thenon-graph databases. If, however, other ones of the graph queries arerelated to results stored at the graph databases, no conversion of thegraph queries to non-graph queries may be needed. In one scenario, forexample, request subsystem 116 may determine where results related tothe graph queries are stored (e.g., which non-graph or graph databasesthe results are stored) and, based on such determination, select thenon-graph databases or the graph databases from which such results areto be obtained. Based on the selection of at least a non-graph database(to obtain at least some results related to a given graph query),request subsystem 116 may convert the graph query to a non-graph querycompatible with the non-graph database.

In some embodiments, data management subsystem 112 may be configured togenerate a graph, one or more subgraphs (e.g., which collectively mayform the graph or a portion thereof), or one or more graph datarepresentations (e.g., nodes, edges, etc., which collectively may formthe graph, the subgraphs, or a portion of the graph/subgraphs). Asindicated herein, in some embodiments, data management subsystem 112 mayutilize one or more graph data models to convert data representations(e.g., stored in SQL tables or other data sources) into graphrepresentations (e.g., compatible with a particular graph database).Responsive to a prediction that a data request will occur in the future,and request subsystem 116 may obtain one or more data subsets that thefuture data request is predicted to seek from one or more data sources.Upon obtaining such data subsets, data management subsystem 112 may usea graph data model to convert the data subsets (e.g., in a non-graphform or other non-compatible representation) into one or more graph datarepresentations or one or more subgraphs including the graph datarepresentations (e.g., compatible with a particular graph database), inaccordance with one or more techniques described herein.

In some embodiments, in response to a prediction of a data request, oneor more data subsets may be obtained from one or more non-graph datasources (e.g., SQL databases or other non-graph data sources), and oneor more other data subsets may be obtained from one or more graph datasources (e.g., graph databases or other graph data sources). As anexample, the data subsets (from the non-graph data sources) may beobtained from the non-graph data sources as non-graph datarepresentations (e.g., SQL rows or columns or other non-graph datarepresentation), and the other data subsets (from the graph datasources) may be obtained from the graph data sources as nodes, edges, orother graph data representations (e.g., compatible with a particulargraph database, not compatible with the graph database, etc.). Datamanagement subsystem 112 may generate one or more subgraphsrepresentative of the data subsets (from the non-graph data sources) andone or more other subgraphs representative the other data subsets (fromthe graph data sources). As an example, with respect to the non-graphsource-obtained data subsets, data management subsystem 112 may use agraph data model to convert the non-graph data representations to graphdata representations that are compatible with the graph database andcompile the graph data representations into the representativesubgraphs. As another example, with respect to the graph source-obtaineddata subsets, data management subsystem 112 may use a graph data modelto compile the graph data representations (of such data subsets) intothe other representative subgraphs. If the graph data representationsare in a form not compatible with the graph database, data managementsubsystem 112 may use the graph data model to convert the non-compatibledata representations to compatible graph data representations andcompile the compatible graph data representations into the otherrepresentative subgraphs.

In some embodiments, data management subsystem 112 may store thesubgraphs (derived from the non-graph data subsets) and the othersubgraphs (derived from the graph data subsets) in a temporary datastorage. In response to obtaining a subsequent data request matching thepredicted data request, request subsystem 112 may obtain the subgraphs,the other subgraphs, or other information from the temporary datastorage and use such obtained information to respond to the subsequentdata request. In some embodiments, request subsystem may extract thedata subsets from nodes, edges, or other graph data representations ofthe obtained subgraphs and return the extracted data subsets to respondto the subsequent data request.

In some embodiments, in response to obtaining a subsequent data requestmatching a predicted data request, request subsystem 116 may generate aquery plan to respond to the subsequent data request. As an example, acandidate query plan may be selected from a collection of query plans(or template query plans) and modified to create the query plan to bespecific for the subsequent data request. In some embodiments, wheresubgraphs or other information related to the predicted data request arestored in a temporary data storage, the query plan may be generated toinclude (i) obtaining the subgraphs or other information from thetemporary data storage, (ii) obtaining other information from other datasources (e.g., graph databases, non-graph databases, etc.). As anexample, based on the query plan, request subsystem 116 may obtain oneor more subgraphs related to the predicted data request from thetemporary data storage and one or more other data subsets (e.g., relatedto the predicted data request, related to the subsequent data request,etc.) from one or more other data sources. Request subsystem 116 may usethe subgraphs (or data sets that the subgraphs represent) and the otherdata subsets to respond to the subsequent data request.

In some embodiments, queries (e.g., graph queries, non-graph queries)that are performed in response to prediction of a data request may be aportion of a set of queries that would have been performed to respond tothe predicted data request had the predicted data request been obtainedfrom a client device. In some embodiments, no performance of one or moreother queries of the set of queries may occur from the prediction of thedata request. As an example, no performance of queries for other mayoccur from the request prediction.

In some embodiments, the subset of results to the queries may be aportion of a set of results that would have been obtained to respond tothe request had the request been obtained from a client device. Forexample, if the set of results are all the results that would have beenprovided on a first web page (e.g., a list of the most relevant resultsor other presentation) returned to a client device (as a response to therequest), the subset of results may be a portion of those resultsprovided on the first web page. In another use case, the set of resultsmay be all the results that would have been obtained to respond to therequest had the request been obtained from the client device. As anexample, even if the other subsets (of the foregoing set of results) areobtained (e.g., via one or more queries responsive to the requestprediction), a determination may be made not to store the other subsets(of the set of subgraphs) in the temporary data storage (e.g., based onfrequency information, cost information, etc., as described herein).

In some embodiments, the obtainment or storage of subgraphs or otherdata (or the determination not to obtain or store other subgraphs orother data) may be based on frequency information, cost information, orother information. In some embodiments, request subsystem 116 mayperform a selection of a subset of queries to be performed (over otherqueries) based on the frequency information, the cost information, orother information. The frequency information may include informationindicating a frequency of requests matching the request or otherinformation. The cost information may include information indicatingcosts for storing data in the temporary data storage, informationindicating costs for performing respective queries, or otherinformation. Such costs may, for instance, include a monetary cost, acomputer resource cost (e.g., bandwidth or other network resource usageamount or other computer resource cost), or other costs.

A cost/benefit analysis may, for instance, be performed to determinewhich or the amount of results to be obtained or stored responsive tothe request prediction. In one use case, request subsystem 116 maydetermine whether to perform queries (or which queries to perform) basedon their respective costs (e.g., a cost to query a data source fordata), the respective benefits of results obtained from those queries(e.g., a frequency of requests matching the predicted request, which ofthe results have priority over other results based on a requester'spreferences, etc.), the respective costs for storing those results atthe temporary data storage, or other criteria. In a further use case,scores may be assigned to respective queries (before they are executed)based on their respective costs, the respective benefits of resultsobtained from those queries, the respective costs for storing thoseresults at the temporary data storage, or other criteria. As an example,a lower cost to query a data source for data may influence a higherassigned score for a corresponding query (compare to scores for otherqueries). A greater frequency of requests matching the predicted requestmay influence higher assigned scores for the queries related to thepredicted request. A greater likelihood that results derived from onequery will be presented to a requester (e.g., on a user interface overother results derived from other queries based on the requester'spreferences) may influence a higher score for the query (compare toscores for the other queries). Based on their respective assignedscores, request subsystem 116 may determine whether or which of one ormore of the queries are to be performed. As an example, requestsubsystem 116 may select a subset of the queries to be performed basedon the subset of queries having greater scores than the other subsets ofqueries.

In another use case, even if obtained, temporary request subsystem 116may determine whether to store results (or the amount of results to bestored) based on the respective costs for storing those results at thetemporary data storage, the respective benefits of those results, orother criteria. In a further use case, scores may be assigned torespective results (e.g., subsets of results) based on the respectivecosts for storing those results at the temporary data storage, therespective benefits of those results, or other criteria. As an example,a lower cost to store certain subsets of results may influence higherassigned scores for the subset of results. A greater frequency ofrequests matching the predicted request may influence higher assignedscores for the results related to the predicted request. A greaterlikelihood that certain subsets of results will be presented to arequester (e.g., on a user interface over other results based on therequester's preferences) may influence a higher score for the subsets ofresults. Based on their respective assigned scores, request subsystem116 may determine whether or which of the results are to be stored atthe temporary data storage. Request subsystem 116 may, for instance,select a subset of the results (e.g., obtained from the performedqueries) to be stored based on the subset of results having greaterscores than the other subsets of results.

In some embodiments, although results may be obtained or storedresponsive to a prediction of one or more requests (as describedherein), no results may be obtained or stored responsive to a predictionof certain other requests (e.g., even if the probabilities of thoseother requests occurring each satisfies a certainty threshold). As anexample, request subsystem 116 may determine not to perform any queriesresponsive to a prediction of a request based on a cost/benefit analysisperformed with respect to the predicted request (e.g., based onfrequency information, cost information, or other information). Asanother example, request subsystem 116 may determine not to store anyresults obtained from the request prediction based on a cost/benefitanalysis performed with respect to the predicted request (e.g., based onfrequency information, cost information, or other information).

In some embodiments, model management subsystem 114 may be configured toobtain request history information and provide the request historyinformation to a prediction model to train the prediction model. Therequest history information may include (i) a collection of priorrequests (e.g., user-submitted requests for data), (ii) a collection ofprior queries generated from the prior requests (e.g., graph queriesconfigured to be compatible with a graph data model), (iii) timinginformation indicating times at which the prior requests or queries areobtained, (iv) frequency information indicating frequencies of the priorrequests or queries, (v) user information indicating the users (e.g.,non-personally identifiable user identifiers or other identifiers) orthe types of users (e.g., age, gender, location, or other categories ofusers) that submitted the prior requests and which of the prior requestswere submitted by the users or types of users, or (vi) otherinformation.

In some embodiments, the prediction model may be configured to obtain atleast one type of the request history information and predict at leastanother type of the request history information based on the obtainedinformation. As an example, for each prior request or query provided asinput to the prediction model, model management subsystem 114 mayprovide the timing information (indicating times at which the priorrequest or query was obtained), the frequency information (indicatingfrequencies of the prior request or query), the user information(indicating the users or types of users that submitted the priorrequest), or other information related to the prior request or query asreference feedback for the prediction model's prediction of timinginformation, frequency information, user information or otherinformation for the prior request or query to train the predictionmodel. The prediction model may use the reference feedback to assess itspredicted information. As another example, for timing information,frequency information, or user information provided as input to theprediction model, model management subsystem 114 may provide the priorrequests or queries associated with the input information as referencefeedback for the prediction model's prediction of requests or queries totrain the prediction model. The prediction model may use the referencefeedback to assess its prediction of requests or queries. Based on itsassessment of its prediction, the prediction model may update one ormore portions of the prediction model (e.g., by adjusting weights of theprediction model's parameters or other portions of the prediction modelin accordance with whether its prediction was accurate or how accurateits prediction was). In one use case, where the prediction model is aneural network, the neural network may update one or more layers of theneural network based on the neural network's assessment of itsprediction of the additional templates.

In some embodiments, upon a prediction model being trained (or updatedbased on such training), model management subsystem 114 may use theprediction model to predict (i) one or more requests or queries, (ii)timing information for such requests or queries, (iii) frequencyinformation for such requests or queries, (iv) user information for suchrequests or queries, or (v) other information for such requests orqueries. As an example, such predictions may include one or moreparameters of a predicted request, such as search parameters (e.g.,keywords, a content item or identifier/location thereof, logicaloperators, etc.) or other parameters. As another example, suchpredictions may include one or more times of such predicted request (ora subsequent request matching the predicted request), frequencies ofsuch predicted request, users or user types predicted to submit therequest, or other predictions. Based on such predictions, requestsubsystem 116 may obtain one or more subsets of results and store theresults in a temporary data storage (e.g., at a server cache, a webcache, memory cache, or other temporary data storage), as describedherein (e.g., in a converted subgraph form compatible with a graphdatabase or other form). When a predicted request (or a future requestmatching the predicted request) does occur, request subsystem 116 mayobtain one or more of the results from the temporary data storage anduse the obtained results to respond to the occurred predicted request.

Query Set Optimization

In some embodiments, optimization subsystem 118 may be configured toreduce query-related resource usage in a data retrieval process. In someembodiments, optimization subsystem 118 may reduce such query-relatedresource usage by optimizing a query set derived from a data request(e.g., an explicit request from a user or other request), such as aquery set into which a graph query (or other query related to the datarequest) is transformed. In some embodiments, such query setoptimizations may include removal of a query operator linking multiplequeries from a query set, merging of multiple queries of a query setinto a single query, removal of one or more queries from a query set, orother optimizations. Such optimizations may be performed based on aprediction of a satisfiability issue (e.g., related to combining resultsderived from certain queries), incompatibility issues, or other issuesto avoid or mitigate such issues or negative impacts of such issues.

In some embodiments, request subsystem 116 may obtain and processmultiple data requests (e.g., from one or more user devices) anddetermine whether the multiple data requests seek common target data(e.g., in which at least a portion of the data sought by the datarequests is the same among the data requests). Based on a determinationthat the data requests seek common target data, request subsystem 116may generate one or more queries (e.g., as part of a query set), whereeach of the queries is configured for obtaining at least a portion ofthe data commonly sought by the data requests such that this one querymay be used to obtain the common data portion to respond to all the datarequests. In some embodiments, request subsystem 116 may generate thequeries such that at least one of the queries is configured forobtaining a first set of commonly-sought data from a first source and atleast another one of the queries is configured for obtaining a secondset of commonly-sought data from a second source. As an example, a givenquery for obtaining the first set of commonly-sought data from the firstsource may be configured to be compatible with the first source,compatible with the first source and not compatible with the secondsource, etc. As another example, a given query for obtaining the secondset of commonly-sought data from the second source may be configured tobe compatible with the second source, compatible with the second sourceand not compatible with the first source, etc. In response to obtainingthe sets of commonly-sought data from the different sources (e.g., thefirst source, the second source, etc.), request subsystem 116 maycombine the sets of commonly-sought data and return the combined sets torespond to each of the data requests.

In some embodiments, request subsystem 116 may determine that multipledata requests (e.g., obtained from one or more user devices) each seektwo or more values associated with two or more attributes common to allthe data requests. As an example, the data requests may be determined tocollectively seek (i) the names of individuals in group A, the names ofindividuals in group B, etc. and (ii) the addresses of individuals ingroup A, the addresses of individuals in group B, etc. In one use case,a first data request may seek the name and address of a first individualin one of the groups, a second data request may seek the name andaddress of a second individual in one of the groups, and so on. In someembodiments, based on a determination (i) that values associated with afirst common attribute (e.g., name) is obtainable from a first datasource and (ii) that the values associated with a second commonattribute (e.g., address) is obtainable from a second data source,request subsystem 116 may generate one or more queries (e.g., as part ofa query set), where at least one of the queries is configured to obtainthe values associated with the first common attribute from the firstdata source, and where at least another one of the queries is configuredto obtain the values associated with the second common attribute fromthe second data source. In some embodiments, for each of the datarequests, request subsystem 116 may join a requested value obtained fromthe first data source and a requested value obtained from the seconddata source and return the joined requested values to respond to thedata request. In one scenario, for example, although there may be anumber of data requests (e.g., tens of requests, hundreds of requests,thousands of requests, etc.) that each seek a name and address of adifferent individual, one query may be generated to obtain the names forall those data requests from the first data source, and another querymay be generated to obtain the addresses for all those data requestsfrom the second data source. Upon obtaining at least some of the namesand addresses via performance of the two queries, the name and addressfor each individual may be joined and returned to respond to thecorresponding data request (i.e., that sought the name and address forthat particular individual).

In some embodiments, after obtaining a first request seeking one or morevalues associated with one or more attributes, request subsystem 116 maywait a predetermined amount of time before generating or performing oneor more queries configured to obtain data to respond to the firstrequest. As such, the wait period allows for other requests (seekingvalues associated with at least one attribute common with the attributessought in the first request) to be obtained, thereby reducing theoverall number of queries that need to be performed to respond to thefirst request and the other requests. In some embodiments, requestsubsystem 116 may determine whether the predetermined amount of time haspassed (e.g., since obtaining a given request). If the predeterminedamount of time has not passed, request subsystem 116 may delaygeneration or performance of one or more queries configured to obtaindata for the given request. On the other hand, if the predeterminedamount of time has passed, request subsystem 116 may determine whichother requests have been obtained since obtaining the given request. Forsuch the given request and other requests seeking values associated withat least one attribute as the values sought in the given request,request subsystem 116 may generate and cause performance of one or morequeries that are each configured to obtain the values associated with acommon attribute sought by the foregoing requests.

In some embodiments, request subsystem 116 may set a predeterminedamount of time (for a wait period described herein) based on requesthistory information, such as (i) a collection of prior requests, (ii) acollection of prior queries generated from the prior requests, (iii)timing information indicating times at which the prior requests orqueries are obtained, (iv) frequency information indicating frequenciesof the prior requests or queries, (v) user information indicating theusers or the types of users that submitted the prior requests and whichof the prior requests were submitted by the users or types of users, or(vi) other information. In some embodiments, a predetermined amount oftime may be set for each category of request, where requests arecategorized based on the content sought by the requests, the users ortypes of users (e.g., age, gender, location, or other categories ofusers), or other criteria. In some embodiments, such a predeterminedamount of time may be determined for a given request in response toobtaining the request (e.g., in real-time). As an example, requestsubsystem 116 may determine a category of the request in response toobtaining the request and then determine the predetermined amount oftime to be used for the wait period (e.g., to allow for more similarrequests to be obtained).

As indicated herein, in some embodiments, request subsystem 116 maypredict that one or more requests will occur in the future, such as aprediction of requests that each seek values associated with commonattributes (e.g., common to all those requests). As discussed herein,such predictions may be based on request history information. In someembodiments, request subsystem 116 may set a predetermined amount oftime for each category of requests based on the predictions related tofuture requests (e.g., predictions regarding when or how frequentrequests in each category will occur). As also discussed herein, aprediction model may be trained based on the request history informationto output indications of such predictions. In some embodiments, theprediction model may be configured to assess its own prediction, and,based on such assessment, the prediction model may update one or moreportions of the prediction model (e.g., by adjusting weights of theprediction model's parameters or other portions of the prediction modelin accordance with whether its prediction was accurate or how accurateits prediction was).

In some embodiments, request subsystem 116 may obtain a data requestfrom a user device or other source and generate one or more queriesbased on the data request. In some embodiments, request subsystem 116may generate a graph query based on the data request. In response toobtaining the graph query, optimization subsystem 118 may transform thegraph query to a query set having multiple queries and query operatorslinking the queries (e.g., unions, joins, or other query operators). Insome embodiments, the graph query may be compatible with a graphdatabase, and the graph query may be used to generate multiple queriesfor the query set that are compatible with one or more target datasources. As an example, a query compatible with a data source can beexecuted to retrieve data from the data sources in accordance with adatabase management system of the data source. As generated, in someembodiments, the multiple queries may be compatible with the target datasources, but not compatible with the graph database (with which thegraph query is compatible). In some embodiments, based on the datarequest, request subsystem 116 may generate a first recursive query(e.g., a graph recursive query compatible with a graph database).Request subsystem 116 may transform the first recursive query into oneor more second recursive queries (e.g., for the query set). As anexample, each of the second recursive queries may be configured to becompatible with a target data source such that, upon execution of one ofthe second recursive queries by a target computer system (e.g., hostingthe target data source), the executed recursive query causes the targetcomputer system to generate multiple queries from the second recursivequery and to execute the multiple queries to obtain data relevant to thedata request from the target data source.

In some embodiments, with respect to the foregoing query set,optimization subsystem 118 may predict one or more issues related to oneor more portions of the query set and perform one or more optimizationson the query set based on the predicted issues to update the query set.As such, when the updated query set is executed to satisfy the graphquery (and, thus, the data request), the predicted issues or thenegative impacts of the predicted issues may be avoided or mitigated.

As an example, a data request may ask for the salary of an employee andthe projects on which the employee has been assigned to work. The datarequest may be written as a graph query with the following two patterns(e.g., which reflects how the results of the query are to be stored asdata representations in a graph, where “employee” and “salary” nodes areconnected by a “hasSalary” edge, and “employee” and “project” nodes areconnected by a “worksOn” edge):

?employee:hasSalary ?salary

?employee:worksOn ?project

In one use case, with respect to the foregoing example, mappings (ortemplates defined by such mappings) may indicate the source table(s) ina RDBMS that include the salary or project information, and optimizationsubsystem 118 use the mappings (along with a database-specific querytranslator) to transform the graph query into a query set in thelanguage of the database. In some cases, there may be more than onesource for each relationship, resulting in a query set having a numberof query operators. For example, an employee may work on multipleprojects, and these projects may be stored in multiple tables. In somecases, transformation of the graph query into a query set (to obtainresults from the RDBMS) may generate a UNION for a source correspondingto a pattern and create a Cartesian product of UNIONs for each patternas SQL joins. If there are N patterns in the graph query and M sourcesfor each term, the query transformation may produce N×M UNIONs.Optimization subsystem 118 may reduce the number of UNIONs, simplifyjoins inside a given UNION, or otherwise optimize the query set.

In some embodiments, optimization subsystem 118 may predict one or moresatisfiability issues related to a query set (e.g., an initial query settransformed from a graph query or other query). In some embodiments,based on a prediction of a satisfiability issue (related to combiningresults derived from two of the queries), optimization subsystem 118 mayremove a query operator that links the two queries from the query set orperform other optimizations on the query set to update the query set. Assuch, when system 100 executes the updated query set to satisfy thegraph query (and, thus, the data request), system 100 may avoid ormitigate the satisfiability issue or the negative impacts of thesatisfiability issue, such as (i) the waste of resources used to executeone or more portions of the query set and attempting to combineincompatible results derived from such query set execution, (ii) thedelay resulting from such execution and attempts, or (iii) othernegative impacts.

In some embodiments, where multiple queries in a query set are linked byone or more query operators (e.g., unions, joins, or other queryoperators), optimization subsystem 118 may determine one or more sourcesfor obtaining results for the linked queries. If there are at least twosources from which results are to be obtained for two of the queries(and such results will initially be non-compatible with a graphdatabase), optimization subsystem 118 may assess templates configured toconvert data representations from such sources to graph datarepresentations compatible with the graph database. If optimizationsubsystem 118 determines an incompatibility related to at least two suchtemplates, optimization subsystem 118 may predict a satisfiability issue(related to combining results derived from the two queries) and removethe query operator linking the two queries from the query set or performother optimizations on the query set to update the query set. In one usecase, mappings may define templates for converting rows from the RDBMSto nodes in a graph by creating globally unique identifiers (which maybe referred to as IRIs). As an example, an employee with ID 123 may bemapped to an identifier such as “http://example.org/employee/123.” Ifthe mappings for the two sources use incompatible templates,optimization subsystem 118 may predict that the join results will beempty and can be eliminated. As an example, if one template is in theform of http://example.org/employee/{ID}, but another template is in theform of http://example.org/department/{ID}, then it can be conclude thatthe two templates are incompatible regardless of the ID value. In otherwords, templates have fixed and variable parts, e.g.,“http://example.org/employee/” and “{ID},” respectively. The value ofthe variables before executing a query is not known, but, if the fixedparts of the templates are inconsistent, the inconsistency can be usedto rule out the possibility of any join between the two templates.

In some embodiments, optimization subsystem 118 may determine data typescorresponding to results for queries linked by one or more queryoperators in a query set, and perform optimizations on the query setbased on one or more incompatibilities related to the data types. Insome embodiments, optimization subsystem 118 may determine that a firstdata type used to store a first set of results (from one of the linkedqueries of the query set) in a graph database is incompatible with asecond data type used to store a second set of results (from another oneof the linked queries) in the graph database. Based on the data typeincompatibility determination, optimization subsystem 118 may predict asatisfiability issue (related to combining results derived from the twoqueries) and remove the query operator linking the two queries from thequery set or perform other optimizations on the query set to update thequery set. In one scenario, for example, columns in the tables may bemapped to primitive values in the graph (e.g., integer, string, date,etc.) instead of IRIs. If a query attempts to join two incompatibletypes (e.g., integer and date) from two tables, such an attempt willfail (in this scenario). As such, if it is determined that such datatypes will be used to respectively store the two sets of data,optimization subsystem 118 may predict the satisfiability issue (e.g.,the failure to join the two incompatible types) and perform theappropriate optimizations (e.g., removal of the corresponding queryoperator, supplementing a different operator in in lieu of the queryoperator, etc.).

In some embodiments, based on a prediction of one or more satisfiabilityissues related to a query set, optimization subsystem 118 may predictone or more additional satisfiability issues related to the predictedsatisfiability issues and to the query set. Optimization subsystem 118may remove one or more query operators related to the additionalsatisfiability issues or perform other optimizations on the query set.In some embodiments, first, second, and one or more other queries may belinked by query operators in a query set. Based on a prediction of afirst satisfiability issue related to the first and second queries(e.g., related to combining results derived from the first and secondqueries), optimization subsystem 118 may remove a first query operatorlinking the first and second queries in the query set or otherwisemodify the query set portion including the first and second queries(e.g., to exclude the first query operator or perform other changes).Based on the prediction of the first satisfiability issue, optimizationsubsystem 118 may predict one or more other satisfiability issues (e.g.,related to combining results derived from the first query and at leastone of the other queries, results derived from the second query and atleast one of the other queries, etc.). Based on the prediction of theother satisfiability issues, optimization subsystem 118 may remove asecond query operator linking two or more of the first, second, or otherqueries or otherwise modify the query set portion including the two ormore queries (e.g., to exclude the second query operator or performother changes). In one use case, optimization subsystem 118 may modifyone or more portions of the query set (e.g., to exclude one or morequery operator or perform other changes) to optimize forunsatisfiability propagation. As an example, with respect to the graphquery that has the two patterns “?employee:hasSalary ?salary” and“?employee:worksOn ?project,” optimization subsystem 118 may predictother issues likely to propagate from the patterns if it determines thatthere are satisfiability issues related to the two foregoing patterns(e.g., the results derived from the queries generated from such patternscannot be joined). In a further example, responsive to two patternsbeing found to be unsatisfiable (e.g., they cannot be joined),optimization subsystem 118 may determine that the unsatisfiabilitypropagates through the query set even if there may be other satisfiablepatterns. If, for instance, another pattern is added to the graph query(e.g., to attempt to retrieve an employee name), optimization subsystem118 may determine that no results will be returned because the first twopatterns did not join.

In some embodiments, optimization subsystem 118 may perform one or moreself-join eliminations or other optimizations on the query set (e.g.,prior to transmitting the queries to one or more RDBMSs or otherdatabase management systems at which the queries are to be executed). Insome embodiments, one of the UNION components cannot be eliminated butit may be simplified to improve performance. If two patterns are mappedto the same source table and there is a unique key for the table, thenoptimization component 118 may generate a single query (e.g., SELECTemployee, salary, project FROM employees) instead of a join (e.g.,SELECT e1.employee, e1.salary, e2.employee, e2.salaray FROM employees ASe1, employees as e2 WHERE e1.employee=e2.employee). In this case,although a SQL optimizer at the query executing database managementsystem may perform this kind of transformation, the complexity ofgenerated SQL queries go beyond what SQL optimizers can handle as morepatterns are added to the query. Queries with too many join conditionsand expressions in the SQL WHERE clause increases the SQL optimizer'ssearch space exponentially resulting in the optimizer to use heuristicsand generate sub-optimal query plans.

In some embodiments, optimization subsystem 118 may provide graphqueries, corresponding query sets (derived from the graph queries), orother information to a prediction model to cause the prediction model topredict (i) one or more issues related to each of the correspondingquery sets, (ii) optimizations for each of the corresponding query sets,or (ii) other information. As an example, such issues may include one ormore satisfiability issues (e.g., related to combining results derivedfrom certain queries), incompatibility issues, or other issues. Suchquery set optimizations may include removal of a query operator linkingmultiple queries from a query set, merging of multiple queries of aquery set into a single query, removal of one or more queries from aquery set, or other optimizations. In some embodiments, with respect toeach of the corresponding query sets, optimization subsystem 118 mayprovide one or more reference issues or optimizations for thecorresponding query set to the prediction model as reference feedbackfor the prediction model's prediction of the issues or optimizations totrain the prediction model. As an example, the reference issues oroptimizations may be provided as reference feedback to cause theprediction model to assess the predicted issues or optimizations againstthe reference issues or optimizations. The prediction model may use thereference issues or optimizations to assess its prediction of the issuesor optimizations. Based on its assessment of its prediction, theprediction model may update one or more portions of the prediction model(as described herein).

In some embodiments, optimization subsystem 118 may provide graphqueries or other information to a prediction model to cause theprediction model to predict a query set for each of the graph queries.In some embodiments, with respect to each of the graph queries,optimization subsystem 118 may provide a reference query set for thegraph query to the prediction model as reference feedback for theprediction model's prediction of the query set to train the predictionmodel. As an example, the reference query set may be provided asreference feedback to cause the prediction model to assess the predictedquery set against the reference query set. The prediction model may usethe reference query set to assess its prediction of the query set. Basedon its assessment of its prediction, the prediction model may update oneor more portions of the prediction model (as described herein).

In some embodiments, upon a prediction model being trained (or updatedbased on such training), optimization subsystem 118 may use theprediction model to determine one or more (i) issues related to aninitial query set derived from a graph query (or other query) or (ii)optimizations for the initial query set. As an example, optimizationsubsystem 118 may provide the graph query or the initial query set asinput to the prediction model to obtain a prediction of (i) the issuesrelated to the initial query set, (i) the optimizations for the initialquery set, or (iii) the optimized query set. In one use case, responsiveto such input, the prediction model may output the optimized query set,indications of such issues, or indications of such optimizations (e.g.,instructions for such optimizations or other indications). In anotheruse case, optimization subsystem 118 may use the indications of theissues or optimizations to transform the initial query set into theoptimized query set.

Displaying Query Response Results

Presentation subsystem 120 may be configured to cause display of queryresults or other information. Presentation subsystem 120 may beconfigured to cause the display of the query results based on the graphdata model templates, the predictions of requests for query results, thesubsets of results obtained responsive to the request predictions, thesubsets of results stored in sub-graphs in temporary data storage, orother information. The displayed query results may include one or morefields in one or more views of a graphical user interface or otherinterfaces. The graphical user interface may be displayed on one or moreclient devices 104, or other computing systems. In some embodiments, thedisplay may include graphical, textual, or other representations. Insome embodiments, the display may include a sub-map, a map, or otherviews of a graph data model. In some embodiments, the display mayinclude provision of one or more textual and/or graphical fields invarious views of the graphical user interface, or other displays.

In some embodiments, presentation subsystem 120 may be configured tocommunicate with the graphical user interface to facilitate entry orselection of information from a user. For example, as described herein,in some embodiments, a given node or edge from a prediction model may beadded to a graph based on a user confirmation entered or selected viathe graphical user interface with respect to adding the given node oredge. An indication of the user confirmation may be provided bypresentation subsystem 120 to the prediction model as reference feedbackregarding the prediction model's generation of the given node or edge.In some embodiments, a user declination with respect to adding the givennode or edge may be obtained by presentation subsystem 120 via thegraphical user interface. Responsive to a user declination, the givennode or edge may not be added to the graph. An indication of the userdeclination may be provided to the prediction model by presentationsubsystem 120 as reference feedback regarding the prediction model'sgeneration of the given node or edge.

In some embodiments, presentation subsystem 120 may be configured tocommunicate with a graphical user interface to facilitate expansion andcontraction, pop up, and/or other display of one or more menus, fields,and/or other objects within or adjacent to one or more of the otherfields. In some embodiments, presentation subsystem 120 may cause suchdisplays responsive to pointing, clicking, or hovering over a specificportion of the display with a pointer or other indicator by a user. Insome embodiments, the expanded fields, the pop-up fields, additionalmenu items, and/or other objects display additional complimentary orinformation that corresponds to the query results to a user.

Examples Flowcharts

FIGS. 12-14 are example flowcharts of processing operations of methodsthat enable the various features and functionality of the system asdescribed in detail above. The processing operations of each methodpresented below are intended to be illustrative and non-limiting. Insome embodiments, for example, the methods may be accomplished with oneor more additional operations not described, or without one or more ofthe operations discussed. Additionally, the order in which theprocessing operations of the methods are illustrated (and describedbelow) is not intended to be limiting.

In some embodiments, the methods may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, or other mechanismsfor electronically processing information). The processing devices mayinclude one or more devices executing some or all of the operations ofthe methods in response to instructions stored electronically on anelectronic storage medium. The processing devices may include one ormore devices configured through hardware, firmware, or software to bespecifically designed for execution of one or more of the operations ofthe methods.

FIG. 12 shows a flowchart of a method 1200 of generating a graph via aprediction model, in accordance with one or more embodiments. In someembodiments, the prediction model may include a neural network, amachine learning model, or other prediction model.

In an operation 1202, first modeling information may be obtained. Thefirst modeling information may be related to a first graph data model.The first modeling information may include first templates forconverting first data representations not compatible with a first graphdatabase to graph data representations compatible with the first graphdatabase. In some embodiments, operation 1202 may include obtaininggraph information related to nodes and edges of a graph in the givengraph database. The graph information may indicate data representationsets. Each data representation set of the data representation sets mayinclude nodes and edges connecting the nodes. Operation 1202 may beperformed by a graph generation subsystem that is the same as or similarto data management subsystem 112, in accordance with one or moreembodiments.

In an operation 1204, one or more templates of the first templates andthe first data representations may be provided to a prediction model.The prediction model may predict one or more additional templates of thefirst templates. The prediction model may be configured to perform theprediction of the additional templates without reliance on theadditional templates. In some embodiments, operation 1204 may include,for each data representation set of the data representation sets,providing one or more nodes or edges of the data representation set tothe prediction model. The prediction model may predict one or moreadditional nodes or edges of the data representation set. The predictionmodel may be configured to perform the prediction of the additionalnodes or edges without reliance on the additional nodes or edges.Operation 1204 may be performed by a graph generation subsystem that isthe same as or similar to data management subsystem 112, in accordancewith one or more embodiments.

In an operation 1206, the additional templates of the first templatesmay be provided to the prediction model. These templates may be providedas reference feedback for the prediction model's prediction of theadditional templates. This feedback may train the prediction model. Insome embodiments, operation 1206 may include, for each datarepresentation set of the data representation sets, providing theadditional nodes or edges of the data representation set to theprediction model as reference feedback for the prediction model'sprediction of the additional nodes or edges to train the predictionmodel. Operation 1206 may be performed by a model subsystem that is thesame as or similar to model management subsystem 114, in accordance withone or more embodiments.

In an operation 1208, a collection of data representations from a givendatabase may be provided to the prediction model. These datarepresentations may allow the prediction model to generate one or moretemplates for a given graph data model for converting the givendatabase's data representations into graph data representations for agiven graph database. In some embodiments, the graph datarepresentations of the given graph database include graph nodes or edgesconnecting the graph nodes. Operation 1208 may be performed by a graphgeneration subsystem that is the same as or similar to data managementsubsystem 112, in accordance with one or more embodiments.

In some embodiments, operation 1208 may include, performing, via theprediction model, traversal of a graph to process nodes or edges of thegraph. This may cause the prediction model to generate new nodes oredges for the graph. The new nodes or edges from the prediction modelmay be added to the graph. A given node or edge from the predictionmodel may be added to the graph based on the traversal of the graph. Insome embodiments, the given node or edge from the prediction model maybe added to the graph based on a user confirmation with respect toadding the given node or edge. An indication of the user confirmationmay be provided to the prediction model as reference feedback regardingthe prediction model's generation of the given node or edge. In someembodiments, a user declination with respect to adding the given node oredge may be obtained. Responsive to a user declination, the given nodeor edge may not be added to the graph. An indication of the userdeclination may be provided to the prediction model as referencefeedback regarding the prediction model's generation of the given nodeor edge.

In some embodiments, method 1200 may further include obtaining secondmodeling information related to a second graph data model. The secondmodeling information may include second templates for converting seconddata representations not compatible with a second graph database tograph data representations compatible with the second graph database. Insome embodiments, the first data representations and the second datarepresentations may be compatible with a same database. In someembodiments, the first data representations may be compatible with afirst non-graph database. The second data representations may becompatible with a second non-graph database and not compatible with thefirst non-graph database. In some embodiments, the first datarepresentations or the second data representations may be compatiblewith at least one graph database.

In some embodiments, method 1200 further includes providing one or moretemplates of the second templates and the second data representations tothe prediction model. The prediction model may predict one or moreadditional templates of the second templates. The prediction model maybe configured to predict the additional templates of the secondtemplates without reliance on the additional templates of the secondtemplates. In some embodiments, method 1200 further includes providingthe additional templates of the second templates to the prediction modelas reference feedback for the prediction model's prediction of theadditional templates of the second templates to train the predictionmodel.

FIG. 13 shows a flowchart of a method 1300 of reducing data retrievaldelays via prediction-based generation of data subgraphs, in accordancewith one or more embodiments.

In an operation 1302, a prediction that a data request will occur in thefuture may be made. The prediction may be based on prior queriescompatible with a graph data model. Operation 1302 may be performed by arequest subsystem that is the same as or similar to request subsystem116, in accordance with one or more embodiments.

In an operation 1304, one or more subgraphs may be generated. Thegeneration of the subgraphs may be based on the graph data model. Thesubgraphs may be representative of data subsets of a data set. The dataset may be data that the data request is predicted to seek. Thesubgraphs may be generated in response to the prediction of the datarequest. Operation 1304 may be performed by a request subsystem that isthe same as or similar to request subsystem 116, in accordance with oneor more embodiments.

In an operation 1306, the subgraphs may be stored in a temporary datastorage. Operation 1306 may be performed by a request subsystem that isthe same as or similar to request subsystem 116, in accordance with oneor more embodiments.

In an operation 1308, a subsequent data request matching the predicteddata request may be obtained. The subsequent data request may beobtained subsequent to the storage of the subgraphs. Operation 1308 maybe performed by a request subsystem that is the same as or similar torequest subsystem 116, in accordance with one or more embodiments.

In an operation 1310, the subgraphs may be obtained from the temporarydata storage. The subgraphs may be obtained based on the subsequent datarequest matching the predicted data request. A query plan may begenerated based on the subsequent data request matching the predicteddata request. The query play may be generated to respond to thesubsequent data request. The query plan may be generated to includeobtaining data from the temporary data storage based on the subsequentdata request matching the predicted data request. Operation 1310 may beperformed by a request subsystem that is the same as or similar torequest subsystem 116, in accordance with one or more embodiments.

In an operation 1312, the subgraphs may be used to respond to thesubsequent data request. As an example, the data subsets may beextracted from nodes and edges of the one or more subgraphs, and theextracted data subsets may be returned to respond to the subsequent datarequest. Operation 1312 may be performed by a request subsystem that isthe same as or similar to request subsystem 116, in accordance with oneor more embodiments.

FIG. 14 shows a flowchart of a method 1400 for reducing query-relatedresource usage in a data retrieval process, in accordance with one ormore embodiments. In an operation 1402, a graph query may be obtained.The graph query may be related to a data request or other requests. Thegraph query may include patterns or other information. Operation 1402may be performed by an optimization subsystem that is the same as orsimilar to optimization subsystem 118, in accordance with one or moreembodiments.

In an operation 1404, the graph query may be transformed into a graphquery set. The transformation may be based on a graph data model, thepatterns of the graph query, or other information. The query set mayinclude queries and query operators linking the queries. The queryoperators may include a first query operator linking first and secondqueries of the queries. Operation 1404 may be performed by anoptimization subsystem that is the same as or similar to optimizationsubsystem 118, in accordance with one or more embodiments.

In some embodiments, operation 1404 may include providing graph queriesto a prediction model (e.g., a neural network, a machine learning model,etc.) to cause the prediction model to predict a given query set foreach of the graph queries. At least one of the predicted given querysets may include predicted queries and predicted query operators linkingthe predicted queries. In such embodiments, operation 1404 may includeproviding, with respect to each of the graph queries, a reference queryset for the graph query as reference feedback to the prediction model tocause the prediction model to assess the predicted given query setagainst the reference query set. The prediction model may be updatedbased on the prediction model's assessment of the predicted given queryset. In such embodiments, operation 1404 may include transforming thegraph query to the query set by providing the graph query to theprediction model to obtain the query set and providing the updated queryset to the prediction model as reference feedback to the predictionmodel to cause the prediction model to assess the query set against theupdated query set. The prediction model may be updated based on theprediction model's assessment of the query set.

In an operation 1406, a satisfiability issue related to combiningresults derived from the first and second queries may be predicted. Theprediction may be made prior to execution of the first and secondqueries. In some embodiments, predicting the satisfiability issue mayinclude determining first and second sources for obtaining results forthe first and second queries, and determining an incompatibility relatedto first and second templates. The first template may be configured forconverting data representations from the first source to graph datarepresentations compatible with a graph database. The second templatemay be configured for converting data representations from the secondsource to graph data representations compatible with the graph database.Operation 1406 may be performed by an optimization subsystem that is thesame as or similar to optimization subsystem 118, in accordance with oneor more embodiments.

In an operation 1408, the first query operator may be removed from thequery set. In some embodiments, the first query operator may include aunion linking the first and second queries or a join linking the firstand second queries. The removal may be based on the prediction of thesatisfiability issue or other information. The first query operator maybe removed from the query set to update the query set such that theupdated query set does not include the first query operator. In someembodiments, operations 1406 and/or 1408 may include predicting, priorto execution of a subset of queries of the query set, based on theprediction of the satisfiability issue, another satisfiability issuerelated to combining results derived from the subset of queries. Thesubset of queries may not include the first query or the second query.In such embodiments, operation 1408 may include removing, based on theprediction of the other satisfiability issue, the second query operatorfrom the query set to update the query set such that the updated queryset does not include the second query operator. Operation 1408 may beperformed by an optimization subsystem that is the same as or similar tooptimization subsystem 118, in accordance with one or more embodiments.

In an operation 1410, execution of the updated query set to satisfy thegraph query may be caused. Operation 1410 may be performed by a requestsubsystem that is the same as or similar to optimization subsystem 118,in accordance with one or more embodiments.

In some embodiments, one or more of the operations of method 1400described above may include providing graph queries or correspondingquery sets to a prediction model to cause the prediction model topredict one or more given optimizations for each of the correspondingquery sets. At least one of the predicted given optimizations mayinclude removal of a given query operator linking multiple queries froma given query set, merging of multiple queries into a single query, orremoval of one or more queries from a given query set. Method 1400 mayinclude providing, with respect to each of the corresponding query sets,one or more reference optimizations for the corresponding query set asreference feedback to the prediction model to cause the prediction modelto assess the predicted given optimizations against the one or morereference optimizations. The prediction model may be updated based onthe prediction model's assessment of the predicted given optimizations.Method 1400 may include providing the graph query or an initial queryset derived from the graph query to the prediction model to obtain oneor more optimizations for the initial query set. Method 1400 may includetransforming the graph query to the query set by performing theoptimizations on the initial query set. Method 1400 may includeproviding an indication of the removal of the first query operator tothe prediction model as reference feedback to the prediction model tocause the prediction model to assess the optimizations against theremoval of the first query operator. The prediction model may be updatedbased on the prediction model's assessment of the optimizations.

In some embodiments, one or more of the operations of method 1400described above may include providing given query sets to a predictionmodel to cause the prediction model to predict one or moresatisfiability issues related to each of the given query sets. Method1400 may include providing, with respect to each of the given querysets, one or more reference satisfiability issues for the given queryset as reference feedback to the prediction model to cause theprediction model to assess the predicted satisfiability issues againstthe reference satisfiability issues. The prediction model may be updatedbased on the prediction model's assessment of the predictedsatisfiability issues. Method 1400 may include providing the query setto the prediction model to obtain an indication of the prediction of thesatisfiability issue from the prediction model. Method 1400 may includepredicting, based in the indication from the prediction model, thesatisfiability issue related to combining results derived from the firstand second queries.

In some embodiments, the various computers and subsystems illustrated inFIG. 1 may include one or more computing devices that are programmed toperform the functions described herein. The computing devices mayinclude one or more electronic storages (e.g., data source(s) 132 orother electric storages), one or more physical processors programmedwith one or more computer program instructions, or other components. Thecomputing devices may include communication lines or ports to enable theexchange of information with a network (e.g., network 150) or othercomputing platforms via wired or wireless techniques (e.g., Ethernet,fiber optics, coaxial cable, WiFi, Bluetooth, near field communication,or other technologies). The computing devices may include a plurality ofhardware, software, or firmware components operating together. Forexample, the computing devices may be implemented by a cloud ofcomputing platforms operating together as the computing devices.

The electronic storages may include non-transitory storage media thatelectronically stores information. The electronic storage media of theelectronic storages may include one or both of (i) system storage thatis provided integrally (e.g., substantially non-removable) with serversor client devices or (ii) removable storage that is removablyconnectable to the servers or client devices via, for example, a port(e.g., a USB port, a firewire port, etc.) or a drive (e.g., a diskdrive, etc.). The electronic storages may include one or more ofoptically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),or other electronically readable storage media. The electronic storagesmay include one or more virtual storage resources (e.g., cloud storage,a virtual private network, or other virtual storage resources). Theelectronic storage may store software algorithms, information determinedby the processors, information obtained from servers, informationobtained from client devices, or other information that enables thefunctionality as described herein.

The processors may be programmed to provide information processingcapabilities in the computing devices. As such, the processors mayinclude one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, or other mechanismsfor electronically processing information. In some embodiments, theprocessors may include a plurality of processing units. These processingunits may be physically located within the same device, or theprocessors may represent processing functionality of a plurality ofdevices operating in coordination. The processors may be programmed toexecute computer program instructions to perform functions describedherein of subsystems 112-120 or other subsystems. The processors may beprogrammed to execute computer program instructions by software;hardware; firmware; some combination of software, hardware, or firmware;or other mechanisms for configuring processing capabilities on theprocessors.

It should be appreciated that the description of the functionalityprovided by the different subsystems 112-120 described herein is forillustrative purposes, and is not intended to be limiting, as any ofsubsystems 112-120 may provide more or less functionality than isdescribed. For example, one or more of subsystems 112-120 may beeliminated, and some or all of its functionality may be provided byother ones of subsystems 112-120. As another example, additionalsubsystems may be programmed to perform some or all of the functionalityattributed herein to one of subsystems 112-120.

Although the present invention has been described in detail for thepurpose of illustration based on what is currently considered to be themost practical and preferred embodiments, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed embodiments, but, on the contrary, is intendedto cover modifications and equivalent arrangements that are within thescope of the appended claims. For example, it is to be understood thatthe present invention contemplates that, to the extent possible, one ormore features of any embodiment can be combined with one or morefeatures of any other embodiment.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

-   1. A method comprising: obtaining first modeling information related    to a first graph data model, the first modeling information    comprising first templates for converting first data representations    not compatible with a first graph database to graph data    representations compatible with the first graph database; providing    one or more templates of the first templates and the first data    representations to a machine learning model for the machine learning    model to predict one or more additional templates of the first    templates, the machine learning model being configured to perform    the prediction of the one or more additional templates without    reliance on the one or more additional templates; providing the one    or more additional templates of the first templates to the machine    learning model as reference feedback for the machine learning    model's prediction of the one or more additional templates to train    the machine learning model; and providing a collection of data    representations from a given database to the machine learning model    for the machine learning model to generate one or more templates for    a given graph data model for converting the given database's data    representations into graph data representations for a given graph    database.-   2. The method of embodiment 1, wherein the graph data    representations of the given graph database comprises graph nodes or    edges connecting the graph nodes.-   3. The method of any of embodiments 1-2, further comprising:    obtaining second modeling information related to a second graph data    model, the second modeling information comprising second templates    for converting second data representations not compatible with a    second graph database to graph data representations compatible with    the second graph database; providing one or more templates of the    second templates and the second data representations to the machine    learning model for the machine learning model to predict one or more    additional templates of the second templates, the machine learning    model being configured to predict the one or more additional    templates of the second templates without reliance on the one or    more additional templates of the second templates; and providing the    one or more additional templates of the second templates to the    machine learning model as reference feedback for the machine    learning model's prediction of the one or more additional templates    of the second templates to train the machine learning model.-   4. The method of embodiment 3, wherein the first data    representations and the second data representations are compatible    with a same database.-   5. The method of embodiment 3, wherein the first data    representations is compatible with a first non-graph database, and    wherein the second data representations are compatible with a second    non-graph database and not compatible with the first non-graph    database.-   6. The method of embodiment 3, wherein the first data    representations or the second data representations are compatible    with at least one graph database.-   7. The method of any of embodiments 1-6, further comprising:    obtaining graph information related to nodes and edges of a graph in    the given graph database, the graph information indicating data    representation sets, each data representation set of the data    representation sets comprising nodes and edges connecting the nodes;    for each data representation set of the data representation sets,    providing one or more nodes or edges of the data representation set    to the machine learning model for the machine learning model to    predict one or more additional nodes or edges of the data    representation set, the machine learning model being configured to    perform the prediction of the one or more additional nodes or edges    without reliance on the one or more additional nodes or edges; for    each data representation set of the data representation sets,    providing the one or more additional nodes or edges of the data    representation set to the machine learning model as reference    feedback for the machine learning model's prediction of the one or    more additional nodes or edges to train the machine learning model.-   8. The method of embodiment 7, further comprising: performing, via    the machine learning model, traversal of the graph to process nodes    or edges of the graph to cause the machine learning model to    generate new nodes or edges for the graph; and adding the new nodes    or edges from the machine learning model to the graph.-   9. The method of embodiment 8, further comprising: obtaining a given    node or edge from the machine learning model based on the traversal    of the graph; adding the given node or edge from the machine    learning model to the graph based on a user confirmation with    respect to adding the given node or edge; and providing an    indication of the user confirmation to the machine learning model as    reference feedback regarding the machine learning model's generation    of the given node or edge.-   10. The method of embodiment 8, further comprising: obtaining a    given node or edge from the machine learning model based on the    traversal of the graph; obtaining a user declination with respect to    adding the given node or edge, wherein the given node or edge is not    added to the graph based on the user declination; and providing an    indication of the user declination to the machine learning model as    reference feedback regarding the machine learning model's generation    of the given node or edge.-   11. A method comprising: predicting that a data request will occur    in the future; generating, based on a graph data model, one or more    subgraphs representative of data subsets of a data set, the data set    being data that the data request is predicted to seek, the one or    more subgraphs being generated in response to the prediction of the    data request; causing the one or more subgraphs to be stored in a    temporary data storage; obtaining a subsequent data request matching    the predicted data request, the subsequent data request being    obtained subsequent to the storage of the one or more subgraphs;    obtaining, based on the subsequent data request matching the    predicted data request, the one or more subgraphs from the temporary    data storage; and using the one or more subgraphs to respond to the    subsequent data request.-   12. The method of embodiment 11, wherein using the one or more    subgraphs comprises: extracting the data subsets from nodes and    edges of the one or more subgraphs; and returning the extracted data    subsets to respond to the subsequent data request.-   13. The method of any of embodiments 11-12, further comprising:    obtain a first data subset of the data subsets from one or more    non-graph databases in response to the prediction of the data    request, wherein generating the one or more subgraphs comprises    generating, based on the graph data model, a first subgraph    representative of the first data subset subsequent to the obtainment    of the first data subset, the one or more subgraphs comprising the    first subgraph such that the first subgraph is used to respond to    the subsequent data request.-   14. The method of embodiment 13, further comprising: obtain a second    data subset of the data subsets from one or more graph databases in    response to the prediction of the data request, wherein the one or    more subgraphs comprises a second subgraph representative of the    second data subset such that the second subgraph is used to respond    to the subsequent data request.-   15. The method of embodiment 14, further comprising: generating    graph queries based on one or more search parameters of the    predicted data request; converting, based on the graph data model,    at least one of the graph queries into one or more non-graph    queries; performing the one or more non-graph queries to obtain the    first data subset from the one or more non-graph databases; and    performing at least another one of the graph queries to obtain the    second data subset from the one or more graph databases.-   16. The method of any of embodiments 11-15, further comprising:    generating, based on the subsequent data request matching the    predicted data request, a query plan to respond to the subsequent    data request, the query plan being generated to include obtaining    data from the temporary data storage based on the subsequent data    request matching the predicted data request, wherein obtaining the    one or more subgraphs comprises obtaining, based on the query plan,    the one or more subgraphs from the temporary data storage.-   17. The method of any of embodiments 11-16, further comprising:    generating, based on the subsequent data request matching the    predicted data request, a query plan to respond to the subsequent    data request, the query plan being generated in response to the    subsequent data request, wherein obtaining the one or more subgraphs    comprises obtaining, based on the query plan, the one or more    subgraphs from the temporary data storage and one or more other data    subsets from one or more other data sources, and wherein using the    one or more subgraphs comprises using (i) the data subsets    represented by the one or more subgraphs and (ii) the one or more    other data subsets to respond to the subsequent data request.-   18. The method of any of embodiments 11-17, further comprising:    performing queries in response to the prediction of the data    request, wherein the performed queries are a portion of a set of    queries that would have been performed to respond to the predicted    data request had the predicted data request been obtained from a    client device; and obtaining, based on the queries, the data subsets    of the data set that the data request is predicted to seek, wherein    generating the one or more subgraphs comprises generating the one or    more subgraphs based on the data subsets and the graph data model.-   19. The method of embodiment 18, wherein no performance of one or    more other queries of the set of queries occurs from the prediction    of the data request.-   20. The method of any of embodiments 11-19, further comprising:    providing the prior queries compatible with the graph data model to    a machine learning model to train the machine learning model;    obtaining, from the machine learning model, an indication of the    prediction of the data request subsequent to the training of the    machine learning model; and providing, based on the subsequent data    request matching the predicted data request, an indication of the    subsequent data request as reference feedback to the machine    learning model to further train the machine learning model.-   21. A method comprising: obtaining a graph query related to a data    request, the graph query comprising patterns; transforming the graph    query to a query set based on a graph data model and the patterns of    the graph query, the query set comprising queries and query    operators linking the queries, the query operators comprising a    first query operator linking first and second queries of the    queries; predicting, prior to execution of the first and second    queries, a satisfiability issue related to combining results derived    from the first and second queries; performing, based on the    prediction of the satisfiability issue, one or more optimizations on    the query set to update the query set; and causing execution of the    updated query set to satisfy the graph query.-   22. The method of embodiment 21, wherein performing the one or more    optimizations comprises removing, based on the prediction of the    satisfiability issue, the first query operator from the query set to    update the query set such that the updated query set does not    include the first query operator.-   23. The method of embodiments 22, further comprising: predicting,    prior to execution of a subset of queries of the query set, based on    the prediction of the satisfiability issue, another satisfiability    issue related to combining results derived from the subset of    queries, the subset of queries not including the first query or the    second query; and removing, based on the prediction of the other    satisfiability issue, the second query operator from the query set    to update the query set such that the updated query set does not    include the second query operator.-   24. The method of any of embodiments 21-23, wherein the first query    operator comprises a union linking the first and second queries or a    join linking the first and second queries.-   25. The method of any of embodiments 21-24, wherein predicting the    satisfiability issue comprises: determining first and second sources    for obtaining results for the first and second queries; determining    an incompatibility related to first and second template, the first    template being configured for converting data representations from    the first source to graph data representations compatible with a    graph database, and the second template being configured for    converting data representations from the second source to graph data    representations compatible with the graph database; and predicting    the satisfiability issue based on the incompatibility related to the    first and second templates.-   26. The method of any of embodiments 21-25, wherein predicting the    satisfiability issue comprises: determining, based on the graph data    model, a first data type as a data type for storing a first result    for the first query in a graph database; determining, based on the    graph data model, a second data type as a data type for storing a    second result for the second query in the graph database;    determining an incompatibility related to the first and second data    types; and predicting the satisfiability issue based on the    incompatibility related to the first and second data types.-   27. The method of embodiments 26, wherein a first data    representation corresponding to the first result is stored as a data    type in a first source that is compatible with a data type used to    store a second data representation corresponding to the second    result in a second source.-   28. The method of embodiments 26, wherein a first data    representation corresponding to the first result is stored as a data    type in a first source that is not compatible with a data type used    to store a second data representation corresponding to the second    result in a second source.-   29. The method of any of embodiments 21-28, further comprising:    providing graph queries to a machine learning model to cause the    machine learning model to predict a given query set for each of the    graph queries, at least one of the predicted given query sets    comprising predicted queries and predicted query operators linking    the predicted queries; providing, with respect to each of the graph    queries, a reference query set for the graph query as reference    feedback to the machine learning model to cause the machine learning    model to assess the predicted given query set against the reference    query set, the machine learning model being updated based on the    machine learning model's assessment of the predicted given query    set; transforming the graph query to the query set by providing the    graph query to the machine learning model to obtain the query set;    and providing the updated query set to the machine learning model as    reference feedback to the machine learning model to cause the    machine learning model to assess the query set against the updated    query set, the machine learning model being updated based on the    machine learning model's assessment of the query set.-   30. The method of any of embodiments 21-29, further comprising:    provide graph queries or corresponding query sets to a machine    learning model to cause the machine learning model to predict one or    more given optimizations for each of the corresponding query sets,    at least one of the predicted given optimizations comprising removal    of a given query operator linking multiple queries from a given    query set, merging of multiple queries into a single query, or    removal of one or more queries from a given query set; provide, with    respect to each of the corresponding query sets, one or more    reference optimizations for the corresponding query set as reference    feedback to the machine learning model to cause the machine learning    model to assess the one or more predicted given optimizations    against the one or more reference optimizations, the machine    learning model being updated based on the machine learning model's    assessment of the one or more predicted given optimizations;    providing the graph query or an initial query set derived from the    graph query to the machine learning model to obtain one or more    optimizations for the initial query set; transforming the graph    query to the query set by performing the one or more optimizations    on the initial query set; and providing an indication of the removal    of the first query operator as to the machine learning model as    reference feedback to the machine learning model to cause the    machine learning model to assess the one or more optimizations    against the removal of the first query operator, the machine    learning model being updated based on the machine learning model's    assessment of the one or more optimizations.-   31. The method of any of embodiments 21-30, further comprising:    providing given query sets to a machine learning model to cause the    machine learning model to predict one or more satisfiability issues    related to each of the given query sets; providing, with respect to    each of the given query sets, one or more reference satisfiability    issues for the given query set as reference feedback to the machine    learning model to cause the machine learning model to assess the one    or more predicted satisfiability issues against the one or more    reference satisfiability issues, the machine learning model being    updated based on the machine learning model's assessment of the one    or more predicted satisfiability issues; providing the query set to    machine learning model to obtain an indication of the prediction of    the satisfiability issue from the machine learning model; and    predicting, based in the indication from the machine learning model,    the satisfiability issue related to combining results derived from    the first and second queries.-   32. A tangible, non-transitory, machine-readable medium storing    instructions that when executed by a data processing apparatus cause    the data processing apparatus to perform operations comprising those    of any of embodiments 1-31.-   33. A system comprising: one or more processors; and memory storing    instructions that when executed by the processors cause the    processors to effectuate operations comprising those of any of    embodiments 1-31.

What is claimed is:
 1. A system for providing neural-network-basedgeneration of a graph data model for converting non-graph datarepresentations to compatible graph data representations, the systemcomprising: a computer system that comprises one or more processorsprogrammed with computer program instructions that, when executed, causethe computer system to: obtain graph modeling information related to agraph data model set, the graph modeling information related to eachgraph data model of the graph data model set comprising templates forconverting non-graph data representations in a non-graph database tograph data representations compatible with a graph database; for eachgraph data model of the graph data model set and the non-graph datarepresentations that the graph data model is configured to convert:provide one or more templates of the graph data model's templates andthe non-graph data representations to a neural network; predict, via theneural network, one or more additional predicted templates for the graphdata model, the neural network being configured to perform theprediction of the one or more additional predicted templates withoutreliance on one or more additional templates of the graph data model'stemplates, the prediction being based on the one or more templates ofthe graph data model's templates and the non-graph data representations;and provide the one or more additional templates of the graph datamodel's templates to the neural network as reference feedback for theneural network's prediction of the one or more additional predictedtemplates to train the neural network; and provide a collection ofnon-graph data representations from a given non-graph database to theneural network for the neural network to generate one or more templatesfor a given graph data model for converting non-graph datarepresentations in the given non-graph database into graph datarepresentations compatible with a given graph database.
 2. The system ofclaim 1, wherein the graph data representations compatible with thegiven graph database comprises graph nodes or edges connecting the graphnodes.
 3. The system of claim 1, wherein the computer system is causedto: obtain graph information related to nodes and edges of a graph inthe given graph database, the graph information indicating datarepresentation sets, each data representation set of the datarepresentation sets comprising nodes and edges connecting the nodes; foreach data representation set of the data representation sets: provideone or more nodes or edges of the data representation set to the neuralnetwork; predict, via the neural network, one or more additionalpredicted nodes or edges, the neural network being configured to performthe prediction of the one or more additional predicted nodes or edgeswithout reliance on one or more additional nodes or edges of the datarepresentation set, the prediction being based on the one or more nodesor edges of the data representation set; and provide the one or moreadditional nodes or edges of the data representation set to the neuralnetwork as reference feedback for the neural network's prediction of theone or more additional predicted nodes or edges to train the neuralnetwork.
 4. The system of claim 3, wherein the computer system is causedto: perform, via the neural network, traversal of the graph to processnodes or edges of the graph to cause the neural network to generate newnodes or edges for the graph; and add at least one of the new nodes oredges from the neural network to the graph.
 5. The system of claim 4,wherein the computer system is caused to: obtain a given node or edgefrom the neural network based on the traversal of the graph; add thegiven node or edge from the neural network to the graph based on a userconfirmation with respect to adding the given node or edge; and providean indication of the user confirmation to the neural network asreference feedback regarding the neural network's generation of thegiven node or edge.
 6. The system of claim 4, wherein the computersystem is caused to: obtain a given node or edge from the neural networkbased on the traversal of the graph; obtain a user declination withrespect to adding the given node or edge, wherein the given node or edgeis not added to the graph based on the user declination; and provide anindication of the user declination to the neural network as referencefeedback regarding the neural network's generation of the given node oredge.
 7. The system of claim 1, wherein each graph data model of thegraph data model set is configured to convert database rows to graphdata representations.
 8. A method of providingmachine-learning-model-based generation of a graph data model forconverting non-graph data representations to compatible graph datarepresentations, the method being implemented by a computer system thatcomprises one or more processors executing computer program instructionsthat, when executed, perform the method, the method comprising:obtaining first modeling information related to a first graph datamodel, the first modeling information comprising first templates forconverting first data representations not compatible with a first graphdatabase to graph data representations compatible with the first graphdatabase; providing one or more templates of the first templates and thefirst data representations to a machine learning model; predicting, viathe machine learning model, one or more additional predicted templates,the machine learning model being configured to perform the prediction ofthe one or more additional predicted templates without reliance on oneor more additional templates of the first templates, the predictionbeing based on the one or more templates of the first templates and thefirst data representations; providing the one or more additionaltemplates of the first templates to the machine learning model asreference feedback for the machine learning model's prediction of theone or more additional predicted templates to train the machine learningmodel; and providing a collection of data representations from a givendatabase to the machine learning model for the machine learning model togenerate one or more templates for a given graph data model forconverting the given database's data representations into graph datarepresentations for a given graph database.
 9. The method of claim 8,wherein the graph data representations of the given graph databasecomprise graph nodes or edges connecting the graph nodes.
 10. The methodof claim 8, further comprising: obtaining second modeling informationrelated to a second graph data model, the second modeling informationcomprising second templates for converting second data representationsnot compatible with a second graph database to graph datarepresentations compatible with the second graph database; providing oneor more templates of the second templates and the second datarepresentations to the machine learning model; predicting, via themachine learning model, one or more additional predicted templates, themachine learning model being configured to predict the one or moreadditional predicted templates of the second templates without relianceon one or more additional templates of the second templates, theprediction being based on the one or more templates of the secondtemplates and the second data representations; and providing the one ormore additional templates of the second templates to the machinelearning model as reference feedback for the machine learning model'sprediction of the one or more additional predicted templates of thesecond templates to train the machine learning model.
 11. The method ofclaim 10, wherein the first data representations and the second datarepresentations are compatible with a same database.
 12. The method ofclaim 10, wherein the first data representations are compatible with afirst non-graph database, and wherein the second data representationsare compatible with a second non-graph database and not compatible withthe first non-graph database.
 13. The method of claim 10, wherein thefirst data representations or the second data representations arecompatible with at least one graph database.
 14. The method of claim 8,further comprising: obtaining graph information related to nodes andedges of a graph in the given graph database, the graph informationindicating data representation sets, each data representation set of thedata representation sets comprising nodes and edges connecting thenodes; for each data representation set of the data representation sets:providing one or more nodes or edges of the data representation set tothe machine learning model; predicting, via the machine learning model,one or more additional predicted nodes or edges, the machine learningmodel being configured to perform the prediction of the one or moreadditional predicted nodes or edges without reliance on one or moreadditional nodes or edges of the data representation set, the predictionbeing based on the one or more nodes or edges of the data representationset; providing the one or more additional nodes or edges of the datarepresentation set to the machine learning model as reference feedbackfor the machine learning model's prediction of the one or more predictedadditional nodes or edges to train the machine learning model.
 15. Themethod of claim 14, further comprising: performing, via the machinelearning model, traversal of the graph to process nodes or edges of thegraph to cause the machine learning model to generate new nodes or edgesfor the graph; and adding the new nodes or edges from the machinelearning model to the graph.
 16. The method of claim 15 furthercomprising: obtaining a given node or edge from the neural network basedon the traversal of the graph; adding the given node or edge from theneural network to the graph based on a user confirmation with respect toadding the given node or edge; and providing an indication of the userconfirmation to the neural network as reference feedback regarding theneural network's generation of the given node or edge.
 17. The method ofclaim 15, further comprising: obtaining a given node or edge from theneural network based on the traversal of the graph; obtaining a userdeclination with respect to adding the given node or edge, wherein thegiven node or edge is not added to the graph based on the userdeclination; and providing an indication of the user declination to theneural network as reference feedback regarding the neural network'sgeneration of the given node or edge.
 18. A system for providingmachine-learning-model-based generation of a graph data model forconverting non-graph data representations to compatible graph datarepresentations, the system comprising: a computer system that comprisesone or more processors programmed with computer program instructionsthat, when executed, cause the computer system to: obtain first modelinginformation related to a first graph data model, the first modelinginformation comprising first templates for converting first datarepresentations not compatible with a first graph database to graph datarepresentations compatible with the first graph database; provide one ormore templates of the first templates and the first data representationsto a machine learning model; predict, via the machine learning model,one or more additional predicted templates, the machine learning modelbeing configured to perform the prediction of the one or more predictedadditional templates without reliance on one or more additionaltemplates of the first templates, the prediction being based on the oneor more templates of the first templates and the first datarepresentations; provide the one or more additional templates of thefirst templates to the machine learning model as reference feedback forthe machine learning model's prediction of the one or more additionalpredicted templates to train the machine learning model; and provide acollection of data representations from a given database to the machinelearning model for the machine learning model to generate one or moretemplates for a given graph data model for converting the givendatabase's data representations into graph data representations for agiven graph database.
 19. The system of claim 18, wherein the computersystem is caused to: obtain graph information related to nodes and edgesof a graph in the given graph database, the graph information indicatingdata representation sets, each data representation set of the datarepresentation sets comprising nodes and edges connecting the nodes; foreach data representation set of the data representation sets: provideone or more nodes or edges of the data representation set to the machinelearning model for the machine learning model; predict one or moreadditional predicted nodes or edges, the machine learning model beingconfigured to perform the prediction of the one or more additionalpredicted nodes or edges without reliance on one or more additionalnodes or edges of the data representation set, the prediction beingbased on the one or more nodes or edges of the data represented set; andprovide the one or more additional nodes or edges of the datarepresentation set to the machine learning model as reference feedbackfor the machine learning model's prediction of the one or moreadditional predicted nodes or edges to train the machine learning model.20. The system of claim 19, wherein the computer system is caused to:perform, via the machine learning model, traversal of the graph toprocess nodes or edges of the graph to cause the machine learning modelto generate new nodes or edges for the graph; and add the new nodes oredges from the machine learning model to the graph.